Environmental Challenges in the Joint Border Area of Norway Finland and Russia

REPORTS 41 | 2015
Environmental Challenges in the Joint Border
Area of Norway, Finland and Russia
JUKKA YLIKÖRKKÖ | GUTTORM N. CHRISTENSEN | NIKOLAY KASHULIN | DMITRII DENISOV |
HELÉN JOHANNE ANDERSEN | ELLI JELKÄNEN (EDIT.)

Environmental Challenges in the Joint Border
Area of Norway, Finland and Russia
JUKKA YLIKÖRKKÖ
GUTTORM N. CHRISTENSEN
NIKOLAY KASHULIN
DMITRII DENISOV
HELÉN JOHANNE ANDERSEN
ELLI JELKÄNEN (EDIT.)

REPORTS 41 | 2015
ENVIRONMENTAL CHALLENGES IN THE JOINT BORDER AREA OF NORWAY, FINLAND AND RUSSIA
Centre for Economic Development, Transport and the Environment for Lapland
Layout: Elli Jelkänen
Cover photos: Sergei Sandimirov (top photo), Jukka Ylikörkkö (bottom left),
Guttorm N. Christensen (bottom right)
Maps: Riku Elo
Printing place: Juvenes Print
ISBN 978-952-314-258-9 (print
ISBN 978-952-314-259-6 (PDF)
ISSN-L 2242-2846
ISSN 2242-2846 (print)
ISSN 2242-2854 (online)
URN:ISBN:978-952-314-259-6
www.doria.fi/ely-keskus

Acknowledgements
Special thanks to the following persons for their contribution to the EU Kolarctic ENPI project ”Trilateral Cooperation
on Environmental Challenges in the Joint Border Area.”
Steering group
Seppo Hellsten
Timo Jokelainen
Bente Christiansen
Guttorm N. Christensen
Nikolay Kashulin
Olga Mokrotovarova
Project coordination
Ilona Grekelä
Helén Johanne Andersen
Project personnel
Jukka Aroviita
Emmanuela Daza Secco
Tanja Dubrovin
Elli Jelkänen
Erkki Järvinen
Petri Liljaniemi
Katja Määttänen
Annukka Puro-Tahvanainen
Martti Rask
Juha Riihimäki
Jukka Ruuhijärvi
Erno Salonen
Samuli Sairanen
Ari Savikko
Mikko Tolkkinen
Jukka Ylikörkkö
Per-Arne Amundsen
Paul Eric Aspholm
Katherine Bell
Tore Flatlandsmo Berglen
Cecilie Bye
Laina Dalsbø
Vo Thanh Dam
Renee van Dorst
Bjørg Jenny Engdahl
Geir Dahl-Hansen
Eirik Henriksen
Karin Strand Johannessen
Martin Rognli Johansen
Tiia Kalske
Brianne Kelly
Bjørn Larsen
Liu Li
Marit Mjelde
Birgitte Refsnes
Javier Sanchez-Hernandez
Sigrid Skoglund
Aslak Smalås
Sverre Solberg
Tove Svendby
Anna von Streng Velken
Aleksandra Antsiferova
Vladimir Chizov
Vladimir Dauvalter
Dmitrii Denisov
Tatyana Kashulina
Anna Kosova
Lubov Kudryavtseva
Sergei Makogonyk
Olga Petrova
Natalia Polikarpova
Sergei Sandimirov
Elena Siekkinen
Petr Terentjev
Ingrida Terentjeva
Svetlana Valkova
Oksana Vandysh
Elena Zubova
Marina Zueva
Field, office and laboratory staff from the following organizations
INEP, MAHEM, LAP ELY, SYKE, NILU, METLA, RKTL, FMIF, UiT and SPA ”Typhoon”
This publication has been produced with the assistance of the European Union but the contents of this publication
can in no waybe taken to reflect the views of the European Union.

Contents
Acknowledgements....................................................................................................... 1
Introduction.................................................................................................................... 3
Chapter 1: Climate change in the border area and modelling
of the SO 2
emissions from the Nikel and Zapolyarny facilities................................ 5
1 Introduction............................................................................................................... 6
2 Climate change in the border area........................................................................... 7
3 Emissions, dispersion and deposition of SO 2
from Nikel and
Zapolyarny, model studies......................................................................................... 14
Chapter 2: Classifications of ecological state and environmental health............ 23
1 Introduction............................................................................................................. 24
2 Chemical status...................................................................................................... 25
3 Typology.................................................................................................................. 30
4 Phytoplankton......................................................................................................... 32
5 Periphytic diatoms................................................................................................... 34
6 Zoobenthos............................................................................................................. 37
Chapter 3: The ecological condition of the Pasvik River and Lake Inarijärvi....... 43
1 Introduction............................................................................................................. 44
2 Climate change impacts on hydrology and water level fluctuation......................... 46
3 Toxic substances on the sediments of the Pasvik River......................................... 52
4 Plankton communities of the Pasvik River.............................................................. 60
5 Aquatic macrophytes of Lake Inarijärvi and the Pasvik River ............................... 68
6 Zoobenthos of Lake Inarijärvi and the Pasvik River .............................................. 73
7 Fish communities of the Pasvik River and long-term
malformation tendencies............................................................................................ 80
8 Long-term effects of metal contamination, water regulation,
species invasion and climate change on the fish of the Pasvik River....................... 90
9 Contaminants in fish of the Pasvik River................................................................ 98
Chapter 4: Evaluation and development of the lake monitoring network........... 107
1 Introduction........................................................................................................... 108
2 Water quality......................................................................................................... 109
3 Sediments and paleolimnology..............................................................................116
4 Biology.................................................................................................................. 132
Chapter 5: Influence of pollution and climate variation in
small rivers indicated by freshwater pearl mussels.............................................. 159
1 Freshwater pearl mussel – The environmental storyteller.................................... 160

Introduction
Finland
Lake Inari
!P
Norway
Näätämö
Paatsjoki
Nellim
Km
0 50 100
!P
EU ENPI Project Trilateral Cooperation on Environmental
Challenges in the Joint Border Area was implemented
in years 2011–2014 as a collaboration between
Finnish, Norwegian and Russian environmental
researchers and authorities. This report describes
the research that addressed several current areas of
interest in the Pasvik watercourse area. The Pasvik
watercourse is located in the border area of Finland,
Russia and Norway in the northern Fennoscandia and
the Kola Peninsula and it is important for the water
supply, energy production, fishing, aquaculture, tourism
and recreation.
Trilateral cooperation started in Interreg IIIA Kolarctic
project of 2003–2006 in which Finnish, Russian
and Norwegian environmental authorities and researchers
from a number of research institutes developed
a joint environmental monitoring programme for the
Pasvik River.
Research concentrated on the main water bodies
Lake Inarijärvi and the Pasvik River. The main environmental
changes are caused by metallurgical industry
in Russia and regulation of the Pasvik River for
hydropower. Climate change will also affect the state
of the environment in the future. The aims of the
studies were to develop tools to assess the effects
of harmful substances, water level regulation and climate
change and to illustrate their effects on different
aquatic environments.
The climate change in the border area and the Kola
Peninsula was assessed based on a long time series
of meteorological observations. A clear change towards
increase in temperature and precipitation could
be seen. Transboundary pollution was modeled and
the results show that SO 2
emissions from the facilities
in Nikel and Zapolyarny in the Pechenga area in
Russia move to Finnish and Norwegian territory by air.
The project developed a monitoring programme to assess
the effects and extent of this airborne pollution.
The effects of pollutants, water level regulation and
climate change on the ecological condition of the Pasvik
River and Lake Inarijärvi was studied. Lake Inarijärvi
was found to be in a good state both chemically
and biologically, but parts of the Pasvik River suffer
from pollution. Water level regulation has changed the
ecology of the waterways. Climate change will also
cause changes as the rising water temperature will
shift the fish species community, for instance.
The existing lake monitoring network was developed
further. The small lakes of the border area were
monitored and a better monitoring programme based
on the most representative lakes and sensitive and
cost-effective variables was developed.
The results of the project have wide application.
The raw data is of high quality and can be used in future
scientific publications. The assessment of climate
change in the area and its effect on the regulated water
bodies can be of use internationally.
This is the summary report for the project. The project
Activities produced detailed expert reports on several
different, environmentally relevant subjects in
the Pasvik area, which have been compressed into
chapters of shortened, simplified reports. Original full
text reports are available at www.pasvikmonitoring.
org
!P
Pasvikelva
River Paz
Kirkenes
!P !P
Nikel
Russia
Arctic Circle
!P
Pechenga
Zapoljarnyi
70 °N
60 °N
© ESRI
3

4

Chapter 1: Climate change in the border
area and modelling of the SO 2
emissions
from the Nikel and Zapolyarny facilities
Bjørnevatnet and Nikel. Photo: Juha Riihimäki
5

1 Introduction
The first part of the project dealt with climate change
and transboundary airborne pollutants in the areas
surrounding the Pasvik watercourse.
Climate change affects both the nature and human
population in the border area of Finland, Norway
and Russia. Climate change processes were assessed
with a decades-long time series of meteorological
observations from the Kola Peninsula. The extensive
data set enabled a reliable quantification of
the existing changes in climate of the region. Both
temperature and precipitation seem to be increasing
in the area, and the warming intensity is increasing.
However, it should be noted that the climate change
is not an evenly distributed process as there are differences
between the coastal and continental areas,
for example. Detailed assessments of changes in different
regions are especially important because they
need to be considered in the environmental protection,
economic activities and social development of the
Kola Peninsula.
The main sources of pollutants in the area are the
Pechenganikel mining and metallurgical company’s
smelter in Nikel and roasting plant in Zapolyarny.
Emissions of SO 2
and heavy metals (Ni, Co, Cu, As)
are prominent. Total emissions of SO 2
are about 100
000 tonnes annually, 40 000 tonnes from Zapolyarny
and 60 000 tonnes from Nikel. Diffusive emissions in
Nikel greatly affect areas in the vicinity of the smelter.
Model studies of the sulfur dioxide emissions, dispersion
and deposition from Nikel and Zapolyarny
industrial plants were performed using two different
models, a WRF-Chem model and a TAPM model.
Correct information about the emissions is important
in order to obtain reliable model results. The model
results were compared with monitoring data to verify
model performance.
WRF-Chem represents atmospheric processes in
a very detailed way. The model is most suited to study
processes and specific episodes. Budget routines
were included to investigate the different processes of
loss of SO 2
from the atmosphere. TAPM can be used
to model air pollution for longer time periods and it
was run for the year 2011. Both models can be of use
in obtaining information of the harmful emissions from
the Pechenganikel facilities.
The Pasvik River. Photo S. Grechany.
6

2 Climate change in the border area
ALEKSANDRA ANTSIFEROVA, OLGA MOKROTOVAROVA, ELENA SIEKKINEN
This assessment of climate change in the Kola Peninsula
and the joint border area is based on data of the
monitoring network spanning the whole peninsula. Climate
data from Russian hydrometeorological stations
in Nikel and Janiskoski and from Norwegian automated
meteorological station in Svanvik were used for a
detailed study of the border area.
Manifestations of climate change are extremely
uneven in different areas. Detailed region-by-region
assessments of the observed and expected climate
changes are especially important because they are to
be considered in the course of economic activities in
weather-dependent industries and in the regions’ social
infrastructure development.
The process of global climate change has turned
out to be varied and comprises three periods: warming
in 1910–1945, slight cooling in 1946–1975, and
intensive warming since 1976. Also in the Kola Peninsula
the ambient air temperature has changed during
1936–2012. A time series of spatially-averaged yearly
anomalies (deviations of the climatic norm of mean
values of 1961–1990) of air temperature and linear
trends to describe the tendency (average rate) of temperature
change in different time intervals illustrates
the changes (Figure 1).
The change of annual mean air temperature in the
Kola Peninsula was assessed by the values of linear
trend coefficient for the three monitoring periods: For
the 1 st monitoring period (1936–2012) the linear trend
coefficient amounts to 0.06°С in 10 years. For the 2 nd
(1961–2012) it amounts to 0.3°С in 10 years and for
the 3 rd (1976–2012) it amounts to 0.6°С in 10 years,
i.e. the warming intensity is increasing.
Even in the relatively small territory of Murmansk
Region the change rate of the average air temperature
and precipitation regime is notably different in the
western part from that in the central part or at the sea
coasts. The geographic distribution of the linear trend
coefficients of the mean seasonal anomalies of the air
temperature in the period 1976 through 2012 in the
territory of Murmansk Region is presented in Figure 2.
˚С
4,0
3,0
2,0
1,0
0,0
-1,0
-2,0
-3,0
1936
1940
1944
1948
1952
1956
1960
1964
1968
1972
1976
1980
1984
1988
1992
1996
2000
2004
2008
2012
Figure 1. Anomalies of the annual mean (January–December) air temperature ( о С), averaged
for the Kola Peninsula territory in the monitoring period from 1936 through 2012. The curve
correlates with 11-year sliding averaging. The straight lines show linear trends for the periods
1936–2012, 1961–2012, and 1976–2012.
7

a) Winter b) Spring
70
69
0.66
0.87
0.47
0.83
0.49
0.45
0.63
0.79
0.80
0.42
70
69
0.37
0.37
0.51
0.47
0.54
0.47
0.46
0.43
0.41
0.50
68
67
0.79
0.72
0.65
0.67
0.75
0.67
0.77
0.78
0.58
0.70
0.26
68
67
0.41
0.51
0.41
0.44
0.44
0.45
0.55
0.52
0.66
0.58
0.66
66
0.62
0.53
30 35 40
66
0.51
0.57
30 35 40
c) Summer d) Autumn
70
69
0.33
0.39
0.37
0.32
0.30
0.31
0.32
0.34
0.39
0.34
70
69
0.55
0.64
0.54
0.64
0.61
0.57
0.61
0.61
0.59
0.64
68
67
0.46
0.46
0.33
0.43
0.39
0.48
0.49
0.48
0.44
0.50
0.75
68
67
0.68
0.62
0.58
0.62
0.62
0.72
0.64
0.64
0.62
0.72
0.63
0.42
0.72
0.46
0.67
66
66
30 35 40
30 35 40
Figure 2. The average rate of seasonal air temperature changes ( o C/10 years) in the Kola Peninsula territory according to the
monitoring data from 1976–2012. The maximum increase of the mean air temperature is observed in winter in the west of the Kola
Peninsula. However, in spring and summer the warming intensity in that area is minimal. The geographic distribution of the mean
seasonal air temperature increase is more even in autumn.
Climate change in the border
area in the values of mean
annual, mean seasonal and
extreme air temperatures
The Janiskoski station performs a full range of meteorological
observations with radiological monitoring, it
monitors cross-border pollutant air transfer and samples
precipitations for chemical composition analysis.
Janiskoski, Nikel and Svanvik hydrometeorological
stations’ temperature regimes are shown in Figure
3. In Janiskoski the mean annual air temperature is
-0.7°С. January is the coldest and July is the warmest
month with many-year mean air temperatures of
-13.3°С and +13.6°С, respectively. In Nikel the mean
annual air temperature is +0.2°С. January is the coldest
and July is the warmest month with temperatures
of -10.7°С and +13.1°С, respectively.
Time series describing the air temperature annual
and seasonal anomalies in the period of 1955 through
2012 and linear trends characterizing the tendency
(average rate) of the temperature change in different
time intervals shows that the warming intensity has
been increasing at the both stations. At the same time,
the increase of the annual mean air temperature at
the Nikel station is higher than that at Janiskoski both
yearly and specifically in each season. Just like on an
average in the Kola Peninsula, the coldest 11-year observation
period at the stations Nikel and Janiskoski
was 1976–1988 and the period since 2002 until now
is the warmest.
8

SVANVIK
!(
!( NIKEL
!(
JANISKOSKI
0 50
km
© Maanmittauslaitos, lupa nro 7/MML/15
Figure 3. Temperature regime in the areas of Janiskoski, Nikel and Svanvik hydrometeorological stations
Currently, the period of 1961–1990 is regarded as
the baseline period for calculations, i.e. the climatic
norm. However, there is an opinion that the “baseline”
period should be approximated to the present time,
i.e. the observation period 1971–2000 should be used
(Table 1).
Noticeably, the monthly mean air temperature in
the warmest 11-year period is higher than the climatic
norm in every month except February. In particular,
while there is an increasing trend of the mean
air temperature both yearly and, especially, in winter,
the monthly mean air temperature in February at the
Janiskoski station has been below the climate norm 8
times over the last 11 years, and 2 times the negative
anomaly exceeded the average quadratic deviation.
This confirms once again the climatologists’ warnings
that even during the “global warming” period considerable
negative anomalies of air temperature are possible.
The seasonal division of the year in the Kola Peninsula
is winter: November–March; spring: April, May;
summer: June–August and autumn: September–October.
One of the signs of the seasonal change from
winter to summer and vice versa is a permanent
crossing of 0°С positively (beginning of spring) and
negatively (beginning of winter). The analyses of the
dates of this zero crossing in the period since 1955
show that at the Janiskoski and Nikel stations the duration
of the period with daily mean air temperature
above 0°С is increasing (≈3 days over 10 years). In
other words, winter has begun to start 1.2–0.9 days
later and spring 1.9–2.7 days earlier in ten years.
In the Janiskoski and Nikel stations in the period
1955–2012 the number of days with maximum temperature
extremes has been growing in all seasons.
However, the frequency trends were statistically insignificant.
The frequency trends of the minimal temperature
extremes in all seasons both in the period since
1955 and since 1976 at both stations are negative but
only the trend of the minimal temperature extremes
in winter at the Nikel station is statistically significant.
It seems that the severity of the border area climate
is decreasing. Meteorological data from the automated
Norwegian station Svanvik was used for a more
detailed study of the climate in the border area. However,
the Svanvik data was shorter and had gaps
and some of it had to be restored based on the data of
Nikel. Due to this certain calculation errors should be
taken into account.
9

Table 1. Monthly mean air temperature (˚С) for three periods: the climate norm 1961–1990, 1971–2000, and for the warmest
11-year observation period 2001–2012.
Period I II III IV V VI VII VIII IX X XI XII Year
Janiskoski hydrometeorological station
1961-1990 -14.2 -13.1 -8.3 -2.3 4.0 10.0 13.3 10.9 5.9 0.1 -7.0 -12.0 -1.1
1971-2000 -13.6 -12.3 -7.5 -2.2 3.9 10.2 13.5 10.9 6.1 -0.1 -7.6 -11.7 -0.9
2001-2012 -12.1 -13.7 -7.8 -0.8 5.0 10.4 14.1 11.4 6.8 1.0 -5.9 -9.2 -0.1
Nikel hydrometeorological station
1961-1990 -11.9 -11.1 -6.9 -2.0 3.6 9.6 13.0 11.0 6.5 0.5 -5.7 -9.8 -0.3
1971-2000 -10.9 -9.8 -5.9 -1.7 3.6 9.5 13.2 11.2 6.6 0.4 -5.9 -9.1 0.1
2001-2012 -9.0 -10.5 -5.8 0.0 4.7 9.7 13.8 11.8 7.5 1.7 -4.0 -6.6 1.1
a
b
T°C
0,0
-2,0
-4,0
-6,0
-8,0
-10,0
-12,0
-14,0
-16,0
y = 0,0643x -8,4795
R² = 0,0956
y = 0,1305x -11,83
R² = 0,2632
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
T°C
5,0
4,0
3,0
2,0
1,0
0,0
-1,0
-2,0
-3,0
-4,0
-5,0
y = 0,0775x + 0,3929
R² = 0,1673
y = 0,1058x -0,4468
R² = 0,2858
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
Svanvik
Линейная (Svanvik)
Nikel
Линейная (Nikel)
Svanvik
Линейная (Svanvik)
Nikel
Линейная (Nikel)
c
d
T°C
16,0
15,0
14,0
13,0
12,0
11,0
10,0
9,0
8,0
7,0
6,0
y = 0,0078x + 11,444
R² = 0,0053
y = 0,0229x + 10,854
R² = 0,028
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
T°C
8,0
7,0
6,0
5,0
4,0
3,0
2,0
1,0
0,0
-1,0
-2,0
y = 0,0546x + 3,4286
R² = 0,1094
y = 0,0846x + 2,3556
R² = 0,1686
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
Svanvik
Линейная (Svanvik)
Nikel
Линейная (Nikel)
Svanvik
Линейная (Svanvik)
Nikel
Линейная (Nikel)
Figure 4. Changes in the mean seasonal air temperature (˚С) in (a) winter, b) spring, c) summer, d) autumn) for the monitoring period
1985 through 2012 at the meteorological stations Nikel and Svanvik. Straight lines show linear trends.
10

The mean annual air temperature is lower in Svanvik
than in Nikel. However, the linear trend coefficient
demonstrating the growth rate of the mean annual air
temperature in Svanvik is higher and estimated as
0.9°С in ten years during the period of 1985–2012.
In the same period in Nikel the linear trend coefficient
is almost two times lower and amounts to 0.5 °C in
10 years. The same tendency is observed in all seasons
(Figure 4). In Svanvik the largest increase of the
mean seasonal air temperature is observed in winter
(with the linear trend coefficient for this season equal
to 1.3°С in ten years), and the least increase is observed
in summer (0.2°С in ten years). Statistical significance
can not be determined due to Svanvik’s data
being partly restored.
Precipitation regime’s
changes in the values of
annual, seasonal and daily
maximum total precipitation
The average annual precipitation in the border area
is 500 mm. Table 2 presents the many-year mean
amounts of precipitation per month, as well as annual
total precipitation amounts in the period 1976 through
2012. As a rule, in the summer months the amount
of precipitation is 2–2.5 times larger than that in winter.
In summer precipitation intensity is considerably
higher than that in winter. The daily precipitation of
>10 mm in summer is fairly usual. Such precipitation
may occur several times during one season. In winter,
there are considerably fewer days with precipitation of
>10 mm, not every year; 0.5 mm per day is the most
common.
Since the middle 1970’s an increasing trend of annual
precipitation amount has been observed at the
Janiskoski and Nikel hydrometeorological stations.
This increase is 1.8 mm/month for 10 years at the
Janiskoski station and 2.4 mm/month for 10 years at
Nikel station.
The changes of total precipitation anomalies are
different in different seasons. At the Janiskoski station
an increase of seasonal precipitation is observed in
all seasons except for winter. In spring, summer, and
autumn precipitation is 3–4 mm/month for 10 years.
At the Nikel station an increase of precipitation in all
seasons is observed and the largest increase is in autumn:
6 mm/month for 10 years.
Heavy rain and snowfall create the greatest problems
for various industrial activities. In winter virtually
no changes are observed in the number of days with
extreme precipitation both at Janiskoski and at Nikel.
In spring, the increase in the number of days with extreme
precipitation is greater at Nikel than at Janiskoski.
In summer, slight increase in the number of days
with extreme precipitation is observed at the Janiskoski
station while a decrease is observed at Nikel. In
autumn, certain increase in the number of days with
peak precipitations is observed at the both stations.
Noticeably, the changes in all seasons are statistically
insignificant.
Table 2. Average precipitation (mm) per month, season, and year.
Janiskoski station
month
I
31
II
25
III
26
IV
32
V
40
VI
61
VII
75
VIII
68
IX
50
X
51
XI
36
XII
29
season
winter
147
spring
72
summer
204
autumn
101
winter
147
year 524
Nikel station
month
I
37
II
28
III
28
IV
28
V
32
VI
54
VII
69
VIII
60
IX
48
X
55
XI
40
XII
36
season
winter
169
spring
60
summer
183
autumn
103
winter
169
year 515
11

Wind regime and frequency
of various wind speed grades
A typical feature of the border area wind regime is its
monsoon pattern, i.e. a clear seasonal change in the
prevailing wind directions. In winter southern winds
from the mainland prevail in Nikel and due to the local
terrain differences south-west winds prevail in Janiskoski
(Figure 6). In summer, northern and north-eastern
winds from the Barents Sea prevail in Nikel and
north-eastern winds are the most frequent in Janiskoski.
The annual mean wind speed in Nikel is 3.8 m/s
and fluctuates seasonally within 1 m/s. The wind
speed in Janiskoski is somewhat lower with the annual
mean of 1.8 m/s and the yearly fluctuation of 0.5
m/s.
In Nikel the highest frequency is observed for the
average wind speed within the grade of 2–3 m/s. In
Janiskoski over 46 % of wind accounts for very weak
wind up to 1 m/s. In Nikel the concentrations of contaminants
from the Pechenganikel mining and metallurgical
industry in the ambient air may increase during
the periods of weak wind, or the so-called stale air.
At the Nikel station the number of days with still air
and low speed wind has somewhat increased since
the middle 1970’s, while at the Janiskoski station the
number of stale air cases has decreased.
One of the most important parameters of the wind
regime is the average number of days with stormy
wind (wind speed ≥15 m/s). The number of stormy
wind cases increases in the winter months in the period
of the highest frequency and intensity of cyclone
processes and decreases almost 5 times in summer
(Table 3). Since the middle 1970’s the number of stormy
days has been observed to decrease 3 days in 10
years both at the Nikel and Janiskoski stations.
Conclusions
The climate change in the border area is characterized
by thermal regime changes, an increase of the
mean annual and mean seasonal air temperature in
particular. The intensity of this increase is growing,
and it is more significant in winter. In Nikel and Janiskoski
the frequency of the days with maximum air
temperatures extremes is growing, along with decreasing
of the number of days with minimal temperatures.
Both at Janiskoski and Nikel precipitation is increasing.
The largest increase is observed at the Nikel
station in autumn. In spring and autumn the number
of days with extreme precipitation is increasing in Nikel
and at Janiskoski the number is increasing in all
seasons except winter.
The wind demonstrates monsoon pattern in the
border area. In winter, southern and south-western
winds prevail, while in summer the prevailing wind directions
are north and north-east. At Nikel a decrease
in the number of stormy days and a growing number
of days with low wind speed can be seen in the period
since the middle 1970’s and until the present. Both the
number of days with stormy wind and low speed wind
is decreasing at Janiskoski.
Table 3. Average number of days with gusts of wind 15 m/s or more. The average number of days less than 1 means that
such gusts of wind do not occur every year.
Janiskoski station
month
I
1
II
1
III
1
IV
1
V
1
VI
1
VII
0.2
VIII
0.4
IX
0.5
X
1
XI
1
XII
1
year 10
Nikel station
month
I
9
II
9
III
8
IV
6
V
5
VI
4
VII
2
VIII
2
IX
2
X
7
XI
8
XII
7
year 71
12

W
NW
N
45
40
35
30
25
20
15
10
5
0
NE
E
W
NW
N
40
35
30
25
20
15
10
5
0
NE
E
SW
SE
SW
SE
S
S
W
NW
N
45
40
35
30
25
20
15
10
5
0
NE
E
W
NW
N 40
35
30
NE
25
20
15
10
5
0
E
SW
SE
SW
SE
S
Figure 6. Seasonal frequency of winter (blue) and summer (red) wind directions. In Nikel station (left) the still air frequency is 19%
in winter and 9 % in summer. In Janiskoski station (right) the still air frequency is 27 % in winter and 20 % in summer.
S
References
Oganesyan V.V. 2004: Climate change in Moscow from 1879 to 2002 in the extreme values of temperature and precipitations.
Meteorology and hydrology 9: 31–37. Moscow. (in Russian)
Bulygina, O.N., Korshunova, N.N., Razuvaev, V.N., Shaimardanov, M.Z., Shvets, N.V. 2000: Variability of extreme climate
phenomena in the territory of Russia. Publications of VNIIGMI-IDC 167: 16–32. Obninsk. (in Russian)
Yakovlev, B.A. 1961: Climate of Murmansk Region. Murmansk publishing company. 24–27. (in Russian)
Davydov, A.A., Antsiferova, A.R. 2007: Climate change in Barentsburg from 1960 to 2005 in the values of annual mean temperature
and its extreme values. Integrated research of the Spitsbergen environment, 7: 140–150. Kola Science Center
RAS. Apatity. (in Russian)
13

3 Emissions, dispersion and deposition
of SO 2
from Nikel and Zapolyarny, model
studies
TORE FLATLANDSMO BERGLEN, BJØRG JENNY ENGDAHL, ANNA VON STRENG VELKEN, LIU LI, VO THANH DAM,
ØYVIND HODNEBROG, FRODE STORDAL
The soil in the Russian-Norwegian border area is rich
in metals and minerals. In 1921 nickel was discovered
in the soil near Kolosjoki in the Pasvik Valley, close to
the Norwegian border. A smelter was established in
the 1930s to exploit these nickel resources. Nickel is
an important constituent in stainless steel and hence a
strategically important resource. The nickel producing
facilities were an important target during World War
II. After the war the plants were reconstructed. Now
the plants are owned by Kola Mining and Metallurgical
Company (Kola MMC), a subsidiary of Norilsk-Nickel 1 .
The ore in the border area is rich in heavy metals,
but also contains a fraction of sulphur (5–6 %). The
industrial processes cause large emissions of sulfur
dioxide (SO 2
) and heavy metals. The combined SO 2
emissions from the two facilities are reported to be
over 100 000 tonnes per year, about 40 % from Zapolyarny
and 60 % from Nikel respectively. The emissions
from the briquetting facility in Zapolyarny and the
smelter in Nikel affect air quality in the area, both on a
local and on a regional scale.
NILU has been conducting a monitoring program
in the border area since 1974, funded by Norwegian
authorities (Ministry of Climate and Environment and
Norwegian Environment Agency). At the moment there
are two well equipped monitoring stations operating,
one at Svanvik, 8.5 km to the west of the city of
Nikel and one in Karpdalen, about 30 km north of Nikel
and 20 km north-west of Zapolyarny. Both Svanvik
and Karpdalen monitor SO 2
and meteorology continuously,
in addition there are sampling and analysis
of heavy metals in air and precipitation. There is also
long term monitoring of SO 2
at Viksjøfjell using passive
samplers. These are the stations at the Norwegian
side of the border. In Russia there are air quality monitoring
stations in Nikel and in Zapolyarny using high
resolution monitors 2 . See map in Figure 1 for location
of the nickel producing facilities and the monitoring
stations in the border area.
As already stated, emissions from the nickel producing
facilities in Zapolyarny and Nikel affect the
environment both on a local and on a regional scale.
In order to quantify the impact of these emissions,
there is a need to better understand the sources, dispersion
and loss of the pollution from these facilities.
Atmospheric models constitute a good tool to better
answer this need. In that respect models also constitute
a valuable supplement to monitoring. The main
focus of the NILU studies is to model the emissions,
dispersion, chemical loss and deposition of the SO 2
emitted from the briquetting plant in Zapolyarny and
the smelter in Nikel using two different atmospheric
models, WRF-Chem (Weather Research and Forecasting
with chemistry included) and TAPM (The Air
Pollution Model, developed by CSIRO).
The atmosphere is a complex system that behaves
according to the laws of physics and chemistry. The
basic assumption in modelling is that the atmospheric
processes can be solved mathematically. A model is
a large set of equations that is solved using supercomputers.
However, simplifications, also called parameterizations,
have to be made. Parameterization is
a method to represent equations using variables and
formulas that are easier to solve mathematically.
Both WRF-Chem and TAPM are Eulerian models.
This means that the atmosphere is divided into boxes.
Then the variables are calculated for each grid
box. The models include processes like emissions,
chemical loss, dry deposition (loss onto surfaces),
wet deposition (loss by rain), and transport between
adjacent grid boxes (by wind or convection). For emissions
from point sources it is important to have fine
resolution near the emission point, in these studies
this means the stacks at the smelter, and more coarse
resolution at a regional scale. Figure 2 shows the
model domains applied in the WRF-Chem model with
fine resolution near the Zapolyarny and Nikel facilities
(1 × 1 km 2 grid boxes), then a regional model domain
14
1 http://www.nornik.ru/en/about-norilsk-nickel/operations/kola-mmc [URL 17-Dec-2014]
2 See http://www.kolgimet.ru/index.php?option=com_content&view=article&id=54&Itemid=239 [URL 22-Dec-2014]

Figure 1. Nickel producing facilities (Nikel and Zapolyarny) and monitoring stations for air quality, precipitation quality and meteorology
in the border area between Norway and Russia.
Figure 2. The model domains: domain d01 is the outermost domain, domain d02 is an intermediate/regional domain while domain
d03 covers the area close to Nikel and Zapolyarny.
15

(5 × 5 km 2 grid boxes), and finally an outer model domain
(25 × 25 km 2 grid boxes).
The SO 2
emissions from Nikel can vary considerably
even on a short time scale, from virtually no
emissions to large plumes of flue gas pouring out of
the smelter and stacks. This variation is probably dependent
upon the smelting processes employed but
no details are known. Due to this lack of information
concerning the emissions variation, the model assumed
constant emissions, adding up to 40 000 tonnes
SO 2
per year from Zapolyarny and 60 000 tonnes SO 2
per year from Nikel.
In Nikel a large fraction of the flue gas is emitted
at ground level, i.e. from the smelter buildings. These
emissions affect the air quality in the vicinity of the
smelter and also local air quality in the city of Nikel
(when the wind is coming from the north). The aim of
a stack is to emit pollutants high above the ground so
that the flue gas is diluted when it reaches the ground
(lower concentrations). The proportion of diffusive
ground level emissions is not known. For the Nikel
smelter, the model assumed that 50 % were emitted
at the ground (diffusive emissions) and 50 % from 160
m above ground level (layer 4 in the model).
WRF-Chem model
WRF-Chem (The Weather Research and Forecast
with chemistry included) model has been applied to
study two specific episodes (Engdahl et al. 2014). During
the summer episode in June–July 2007 there were
large emissions combined with stable atmosphere
and weak wind. In this study the period 1.–7. July
2007 was investigated. During winter 2010/11 there
were many episodes with high concentrations in Karpdalen.
Karpdalen is influenced by emissions from
both Zapolyarny and Nikel. The period 23. December
2010–7. January 2011 was investigated.
The WRF-Chem model is able to describe air pollution
on both local and regional scale. A nested grid
was applied to the study area, which was divided into
three domains (Figure 2). The largest domain included
the northern regions of Fennoscandia, northwest
Russia and the Barents Sea with grid box size of 25
× 25 km 2 . The middle domain covered eastern Finnmark,
some regions of Northern Finland and the region
around Nikel with grid box size of 5 × 5km 2 . The
smallest domain concentrated on the immediate vicinity
of the emission sources with grid box size of 1 ×
1 km 2 . In the vertical the atmosphere was divided into
layers, thin layers close to the ground and gradually
increasing layers with altitude.
As input data to the model, prescribed meteorological
data from WRF were applied. In a way, these data
are the same as data used for weather forecast. Data
from WRF were compared with analysis data from European
Centre for Medium Range Weather Forecast,
ECMWF. The WRF data compared well with the analysis
data. This means that the meteorological input
data represent well the meteorological conditions in
the atmosphere, like wind direction and wind speed,
temperature, etc. Especially wind direction and wind
speed, as well as atmospheric stability are important
parameters for dispersion of air pollution. In addition,
wet deposition/rainfall is an important loss process
for sulphur. As described earlier the model assumed
constant emissions of SO 2
, 40 000 tonnes per year
from Zapolyarny and 60 000 tonnes per year from Nikel.
For Nikel 50 % were emitted at ground level and
50 % at 160 m above ground.
During the beginning of the summer episode of 1.–
8. July 2007 there was a high pressure system over
the Barents Sea and a low pressure system south
from the Kola Peninsula. This caused dry weather
and absence of strong winds, only some northeastern
winds were noted. At the end of the episode the
high pressure area had moved eastwards, wind had
grown stronger and blew from the east. The northeastern
winds transported the emissions from Nikel and
Zapolyarny towards the city of Nikel and also some towards
Svanvik. The SO 2
concentrations are generally
highest in these areas near the smelter. But emissions
can disperse over relatively large areas and even far
into Finland, Sweden and Norway, though concentrations
farther away from the sources are much lower.
In the beginning of the winter episode of 23. December
2010–7. January 2011 there was relatively
weak wind from the west, which changed suddenly into
stronger southern wind around the turn of the year.
After a while there was some northern wind, but the
episode ended with southern wind again. Stable conditions
and weak winds transport the emissions slowly
away from the city of Nikel and the local concentrations
north of the smelters are therefore higher.
Comparison with observations is the only way to
verify model performance. Model results were compared
with observations of Svanvik, Nikel (only summer
episode) and Karpdalen (only winter episode) (Figure
3). Observations show a pattern typical for monitoring
stations located close to major emission sources, there
are usually very low concentrations when the wind
16

is coming from other directions than the smelter. The
gas plume is well defined close to the sources and
when the flue gas plume hits the monitoring station,
the concentration suddenly increases.
The modelled concentrations are lower than the
observed maximum concentrations and the variation
in actual concentrations is larger. A possible explanation
for this is that in the real atmosphere the gas plume
from the stacks is well defined, whereas in the model it
will be distributed evenly within the grid boxes (“levelled
out” in a 1 × 1 km 2 grid box). Another explanation
is that the model emissions are not well represented.
The gas is also emitted at the ground level (diffuse
emissions), not only from the stacks, but no reliable
information exists concerning the ground:stack
emission ratio. Especially Svanvik is affected both by
ground level emissions and stack emissions. There is
also a no information about the time variation of the
emissions. In the model constant emissions are assumed
whereas in the real atmosphere the emissions
will vary considerably on a short time scale. Correct
emission information is crucial to obtain more precice
model results.
Plots showing the dispersion in the three model
domains have been elaborated (Figure 4). During the
summer episode the winds were stronger and dispersion
more effective on a regional scale. During the
winter episode there were stable conditions, weak
winds and slow dispersion (results not shown). Short
“films” were also made to show the dispersion during
the summer episode 3 .
A budget quantifying the processes important for
SO 2
was elaborated as a tool in the model development.
These routines were also helpful in the assess-
Figure 3. Modelled and observed concentrations of SO 2
from (a) Svanvik 3. July 2007, (b) Nikel 3. July 2007, (c) Svanvik 3. January
2011, and (d) Karpdalen 3. January 2011. If the observations are close to 0, they are not visible in the plot (blue dots).
3 Please see ”films” at [visited 01-03-2015]:
http://folk.uio.no/torefl/WRF-Chem/Domene1_SO2-SO4_delay10.gif
http://folk.uio.no/torefl/WRF-Chem/Domene2_SO2-SO4_delay10.gif
http://folk.uio.no/torefl/WRF-Chem/Domene3_SO2-SO4_delay10.gif
17

Figure 4. Dispersion of SO 2
for 6. July 2007 at 12h UTC a) model domain 1 (outermost domain), b) model domain 2 (intermediate)
and model domain 3 (innermost domain). Please note different scale in plots (a) and (b) compared to plot (c). Unit: ppb (parts per
billion, 10 -9 , mixing ratio) as sum over the lowermost model layers.
ment of the wet deposition parameterization. Wet deposition
is an important process and has a large effect
on SO 2
and H 2
SO 4
concentrations. Thus a budget was
calculated to determine the contribution from each
single process (dry deposition, wet deposition, transport,
chemistry etc.) within the model grid (largest
domain).
SO 2
emissions and transport into the model domain
are the two most important sources during both summer
and winter episodes. Emissions are positively the
largest source of SO 2
, especially in boxes near Nikel,
and transport from the outer edges of the model domain
are minor. There is a large difference between
the summer and winter episodes concerning the pathways
of loss of SO 2
from the atmosphere. Sunlight,
especially shortwave UV, drives the chemical processes
in the atmosphere (photochemistry) and sunlight
is abundant in summer (midnight sun) and absent
in winter. Sunlight generates OH, which is the most
important oxidant in daytime chemistry. OH also oxidizes
SO 2
to H 2
SO 4
. Chemical loss is the most important
summertime process of loss of SO 2
from the
atmosphere, the others being wet deposition and dry
deposition.
During the winter there is a polar night and total
absence of direct sunlight in the border area. Chemical
loss is minor in wintertime due to low levels of
oxidants in the atmosphere (e.g. OH). Because only
a little sulfuric acid is formed, all of the reactions are
reduced. The main loss processes during winter are
wet deposition and dry deposition.
Deposition, dry or wet, is the final removal process
of sulphur from the atmosphere. Sulphur deposited on
the ground will eventually contribute to acidification.
Oxidation changes SO 2
into H 2
SO 4
but does not remove
it from the atmosphere.
18

TAPM model
TAPM (The Air Pollution Model developed by CSIRO)
was set up for the border area and run for the year
2011, i.e. the meteorological data represented 2011.
The set up concerning model domains and emissions
were similar to the WRF-Chem (see previous section).
Monthly mean and annual mean concentrations
were put to file, as well as results of dry and wet deposition.
In addition hourly mean results for the grid
boxes representing Svanvik, Nikel smelter, city of Nikel
and Zapolyarny were put to file. Model – observation
comparison for hourly mean values at Svanvik is
shown in Figure 5. Annual mean ground level values
for the inner model domain are shown in Figure 6.
For the Svanvik station the model tends to overestimate
the peak maximum values, maximum value in
the model is 3300 µg/m 3 of SO 2
while observations
show maximum 858 µg/m 3 , i.e. the model maximum
is a factor 4 higher than observed. The model also
overestimates the annual mean concentration (model
results 23 µg SO 2
/m 3 vs observations 7,3 µg SO 2
/m 3 ).
It is difficult to compare short term values at Russian
stations since the observations are 20-minutes mean
while the model calculates 1-hr means. For Zapolyarny
and Nikel the model underestimates the observed
values concerning annual mean concentrations (results
not shown).
Dry deposition is the main loss pathway close to
the smelter. The dry deposition results resemble to a
large extent the ground level concentrations (results
not shown). This is logical since dry deposition is a
function of ground level concentration and deposition
velocity. Wet deposition is a function of concentrations
in the column of air, solubility of the compounds,
clouds and rainfall.
To estimate deposition of heavy metals a simple
method has been elaborated. The best information
available concerning deposition of sulphur and deposition
of Ni are the observations from Svanvik and
Karpdalen where both components were sampled
an analyzed simultaneously during the period 1993–
2003. The nss-S:Ni-fraction 4 was about 35 at Svanvik
during this period. The basic idea here is then to
use the observed nss-S:Ni-ratio from 1993–2003 from
Svanvik and Karpdalen and the model calculated deposition
of sulphur from TAPM to estimate deposition
of Ni. In this study a nss-S:Ni-ratio of 25 and 30 was
assumed. This is lower than observed during 1993–
2003. But given that the emission of sulphur has decreased
and emissions of Ni increased since then the
Svanvik SO2 model & observation µg/m³
3500
3000
2500
2000
1500
1000
500
0
1
314
627
940
1253
1566
1879
2192
2505
2818
3131
3444
3757
4070
4383
4696
5009
5322
5635
5948
6261
6574
6887
7200
7513
7826
8139
8452
Model
Observations
Figure 5. Model results and observations (hourly mean values) from the Svanvik monitoring station, counting hours from 1 st January
2011. Unit µg SO 2
/m 3 .
4 nss-S is abbreviation for non sea-salt sulphur
19

Figure 6. Annual mean concentration of SO 2
for the year 2011 for the area close to Nikel and Zapolyarny. Unit: µg/m 3 .
ratio is likely lower now than 20 years ago. Ni-deposition
for 5 stations and 9 lakes in the border area were
estimated. For Svanvik the estimate is lower than
observations, but within a factor 2 of the observed values
of Ni.
Conclusions
Models represent an important tool to better understand
emissions, dispersion and loss of pollutants
emitted from smelter facilities in the border area. Correct
information about the emissions is crucial in order
to obtain reliable model results. Total emissions of
SO 2
is about 100 000 tonnes annually, 40 000 tonnes
from Zapolyarny and 60 000 tonnes from Nikel. Diffusive
emissions in Nikel greatly affect areas in the
vicinity of the smelter. It is also important to represent
wet deposition correctly in the model. Meteorology,
especially wind and stability, are important factors for
dispersion from the smelters.
The WRF-Chem model (Weather Research and
Forecasting with chemistry included) has been set up
for the border area with a nested grid centered on Zapolyarny
and Nikel. The periods 1.–7. July 2007 (during
the summer episode 2007) and 23. December
2010–7. January 2011 (winter with elevated concentrations)
have been investigated. The model tends to
underestimate the concentration in episodes. Budget
routines were included to investigate the different loss
processes. Chemistry is an important loss process in
summertime, but not in winter. Wet deposition and dry
deposition are the main loss processes of sulphur.
Short “films” have been made to show the dispersion.
WRF-Chem does represent atmospheric processes
in a very detailed way. However, it is computationally
demanding. In that respect the model is most
suited to study processes (emissions, dispersion, chemical
loss, dry deposition, wet deposition etc.) and to
study specific episodes (e.g. summer episode 2007)
rather than produce long-term calculations of concentrations
and deposition.
20

TAPM (The Air Pollution Model) was set up for the
border area and run for the year 2011. Both short term
(hour) and long term (month, year) concentration means
were put to file, as well as annual mean of dry and
wet deposition. The model overestimates the observed
values at Svanvik (both hourly mean and annual
mean concentrations), while it underestimates the annual
mean observations for the Zapolyarny and Nikel
stations. The model shows high values to the north
of Nikel, but there are no observations to verify these
results.
A method to estimate deposition of heavy metals
has been elaborated based on model results of deposition
of sulphur and observed sulphur:Ni ratio at
Svanvik and Karpdalen.
TAPM is fast to run on the supercomputer, but there
is no access to the model code (“black box”). Because
of this it is therefore difficult to make sensitivity tests to
investigate and analyze the results.
References
Engdahl, B.J., Velken, A.v.S., Berglen, T.F., Hodnebrog, Ø., and Stordal, F. 2014: Utslipp, spredning og avsetning av SO2
fra Nikel og Zapoljarnij, En WRF-Chem modellstudie, NILU OR 57/2014 (in Norwegian with Summary and figure captions
in English).
Berglen, T.F., Liu, L., and Dam, V.T. 2014: Emissions, dispersion and deposition of SO 2
emitted from Nikel and Zapolyarny
facilities. A TAPM model study, NILU OR 70/2014.
Pechenganikel after the summer episode of
2007. Photo: Espen Aarnes.
Affiliations of the authors: Tore Flatlandsmo Berglen (NILU), Bjørg Jenny Engdahl (NILU, now Meteorological Institute, Norway), Anna
von Streng Velken (NILU, now Norwegian Environment Agency), Liu Li (NILU), Vo Thanh Dam (NILU), Øyvind Hodnebrog (Univ.
of Oslo, now CICERO), and Frode Stordal (Univ. of Oslo)
21

22

Chapter 2: Classifications of ecological
state and environmental health
JUKKA YLIKÖRKKÖ
Vätsäri wilderness. Photo Jukka Ylikörkkö.
23

1 Introduction
When assessing the chemical state of surface waters,
different limit values and standards are applied in different
countries. Similarly the classification of aquatic
ecological status is based on different organisms and
multiple variables. Therefore the risk assessments
and ecological evaluations are often difficult to compare.
The classifications for ecological status and limit
values for concentrations of hazardous substances
are undergoing updating and changes in Europe,
both on national and international level. Intercalibration
and harmonization has been done between the
EU nations. Standards or classifications are often not
suitable for naturally harsh and nutrient poor northern
environments. Here the validity of existing standards
and metrics for lakes is assessed using data collected
in the project.
Literature review and international comparison
concerning the national and international classifications,
typologies, standards and limit values for ecological
state and environmental health has been done
as a background 1 for the following work with project
data. The most valid tools for analysis and reporting
results are selected for trilateral monitoring programme
based on this assessment. These outcomes will
also support the selection of most representative and
cost-effective environmental variables and indicators
for aquatic monitoring.
Biological diversity metrics are compared
between the regions using Kruskal-Wallis
test, which is a non-parametric variance
analysis suitable for small, skewed data and
for groups of unequal size.
Sediment samples.
Photo: Helén Andersen
Field equipment. Photo: Jukka Ylikörkkö
1 See precursory work at www.pasvikmonitoring.org
24

2 Chemical status
In this part water chemistry data from the Pasvik River
(Chapter 3) and lakes in the surrounding regions
(Chapter 4) was used. For reference an additional lake
LN-2 from north-east of Nikel was included. The lakes
can be grouped into 3 areas in relation to Nikel: northeast
lake LN-2 is the most exposed to the smelter’s
airborne emissions, Norwegian lakes in Jarfjord are
further away in the same direction and the rest of lakes
in Finland and Russia are located south or west of
Nikel, upwind of the prevailing wind direction and the
least affected by the airborne emissions. The Pasvik
River runs roughly along this gradient. It receives heavy
metals in wastewater discharge through Kuetsjarvi
and some aerial deposition. All values are measured
from 1 m depth during project period 2012–2013.
The chemical standards assessed here include
the lists of the EU priority substances and northern
EU countries’ specific pollutants, Russian MAHEM,
United States Environmental Agency (USEPA) and
Canada. The latter two standard lists concern criteria
for aquatic life. The work has been done with standards
valid in 2013.
The most common metals have limit values in all
national and the EU standards (Table 4). Cadmium,
chromium, copper, nickel and zinc have often water
hardness-dependent standards. The project lakes’
water hardness in CaCO 3
content was estimated
using calcium and magnesium concentrations. Most
locations had soft water: estimated CaCO 3
< 20 mg/l.
For Kuetsjarvi the hardness was higher: 40 mg/l, and
for LN-2 estimation indicated hard water: 125 mg/l.
Standards for soft water in Table 4 were calculated for
hardness 17 mg/l CaCO 3
, because the models often
do not apply on lower values (CCME 2012).
Project data mean values were compared to chronic
and long-term environmental standards, and data
maximums to maximum allowable concentrations
(MACs) and acute short-term standards.
The 33 EU priority substances that include persistent
organic pollutants, organochloride and organophosphorus
compounds and pesticides were not
analyzed in water in the scope of this project.
Major ions
Lake LN-2 has both the maximum and average
sulphate concentration above all the listed standard
values (Figures 1–2, Table 1). Results from the lakes
in the other regions or in the Pasvik River were substantially
below the lowest standards. Regional maximum
and average values against the lowest standard
are summarized in Table 1 and Figures 1 and 2.
Table 1. The lowest standards for major ions with the corresponding maximum (short-term) or highest average (longterm)
in certain lakes or regions in 2012–2013 sampling period. Standard-exceeding results emphasized.
Lowest standard
(mg)
LN-2/
(mg)
Kuetsjarvi
(mg)
Jarfjord max.
(mg)
Small lakes max.
(mg)
Pasvik max.
(mg)
Na short-term 120 MAHEM 4.3 4.1 4.0 1.6 1.7
Mg short-term 40 MAHEM 10.3 4.4 1.1 1.2 1.3
K short-term 10 MAHEM 0.70 0.95 0.44 0.54 0.50
Cl- short-term 300 MAHEM 5.7 3.9 5.6 1.8 1.6
Cl- long-term 120 Canada 4.0 3.2 - 1.7 1.4
SO4 short-term 100 MAHEM 126.0 34.1 4.6 3.5 6.4
SO4 long-term 50 Canada 118.5 30.0 - 3.3 4.6
25

Figure 1. Maximum measured sulphate in Lake LN-2, regionally
in small lakes, Kuetsjarvi and other parts of the Pasvik River
with the Russian MAC (100 mg/l).
Figure 2. Average sulphate levels in in Lake LN-2, regionally in
small lakes, Kuetsjarvi and other parts of the Pasvik River with
the Canadian alert level for long-term average (50 mg/l).
Metals
Metals are given as dissolved concentrations in filtered
(0.45 µm) samples unless stated otherwise. Nickel
and copper are the main metals emitted by industry in
the study area. Both are found on high levels in the
nearest lakes (LN-2 and Kuetsjarvi) and in some of
the Jarfjord lakes (Table 2, Figures 3–7).
Some of the standards represent added risk in
addition to the background concentration. Regional
background concentrations were not noted in the following
results. The lowest MAC for copper is 1 μg/l
by MAHEM. There are single results from all the lake
groups that exceed this concentration. USEPA acute
standard for soft water (

Figure 3. Maximum measured copper in certain small lakes,
the Pasvik River and Vätsäri area. The lines show short-tem
standard values: USEPA (2.6 μg/l) and Russian MAC (1.0 μg/l)
Figure 4. Average copper in certain small lakes, the Pasvik
River and Vätsäri area. The lines show long-term standard
values for soft water: the UK (1 μg/l), Canada (2 μg/l), Sweden
(4 μg/l) .
Figure 5. Maximum measured nickel in certain small lakes, the
Pasvik River and Vätsäri area. The lines show short-term standard
values: USEPA (105 μg/l) and Russian MAC (10 μg/l).
Figure 6. Average nickel in certain small lakes, the Pasvik
River and Vätsäri area. The lines show long-term standard
values for soft water: USEPA (12 μg/l), EU EQS (20 μg/l) and
Canada (24 μg/l).
Figure 7. Lake LN-2 maximum and average copper and maximum
nickel. Lines show correspondent standard values adjusted
for the lake’s water hardness by the USEPA criteria.
27

Several other measured metal concentrations are
the highest in LN-2 yet below the lowest standard
(Table 3). These include maximum total aluminum and
cadmium.
Aluminum is measured both as total and labile. Total
aluminum maximum concentrations are clearly the
highest close to Nikel, but do not exceed the lowest
standard values.
Manganese MAC by MAHEM (10 µg/l) is exceeded
by many lakes. Considering the past results measured
from streams (Salminen 2005) and the monitored
lakes in the area, 10–20 µg/l still represents the Mn
background level in lakes in the area. The only notably
high manganese concentration is found in Lake
LN-2 (max. 123 µg/l). Manganese is not noted in national
standard lists, but for example USEPA (2010)
refers to results from LC 50
experiments with mussels,
which indicate Mn on the order of tens of milligrams
to be toxic.
Iron MAC by MAHEM (100 µg/l) is also exceeded
by several water bodies. The standard is again rather
low compared to the regional background level (Salminen
2005). In long-term all the iron standards are
met.
There seems to be elevated zinc concentration in
Lake LN-2, which does not meet the lowest standards
in maximum or long-term concentrations. Background
levels in the region’s streams are generally less than
1 µg/l for Zn (Salminen 2005) and the content in all the
other lakes is clearly below the standard.
Nutrients and trophic status
Ammonium, nitrate and phosphate
MAHEM MACs for ammonium (400 μg/l) and nitrate
nitrogen (9100 μg/l) are the lowest of considered standards.
All the measured results were notably below
the standard MACs. Among the lakes the maximum
measured ammonium nitrogen concentration varied
from 9.0 to 60.0 μg/l, in the Pasvik River 9.0–85.0
μg/l, and the maximum nitrate nitrogen 3–49 μg/l in
the lakes and 1–46 μg/l in the river. All the maximum
measured phosphate phosphorus concentrations were
below MAHEM MAC (150 μg/l), varying from 1 to 9
μg/l in the lakes and 1–5 μg/l in Pasvik river.
pH
The measured minimum pH values from the lakes and
the Pasvik River mostly exceeded the 6.5 limit, which
is most often used standard for minimum pH. Lake
Sierramjärvi had lower minimum (5.8), which seems
to be a single anomaly when observed in long time
scale. The lake is in the reference area and minimally
affected by anthropogenic pressures.
COD
The highest measured chemical oxygen demand values
were 6.8 mg/l (Virtuovoshjaur) and 5.8 mg/l in
Pasvik River (Ruskebukta). Russian MAC for COD is
15 mg/l.
Trophic status
Using the Canadian trophic status criteria for mean
total phosphorus, the lakes are ultra-oligotrophic (< 4
μg/l) or oligotrophic (< 10 μg/l). The Pasvik River water
reservoirs are all oligotrophic (< 10 μg/l total P),
except for Ruskebukta, which is meso-eutrophic in
terms of total phosphorus (22 μg/l). This is consistent
with the previous studies and other obtained results
(see Chapter 3).
Conclusions
Russian maximum allowable concentrations and Canadian
or US environmental agency criteria for the
protection of aquatic life are the most comprehensive
national standard lists and therefore useful in assessing
chemical status. Moreover these are most often
the lowest and strictest standards. For example the
USEPA criteria are set as a result of lethal dose and
effect dose studies with zooplankton, some benthic
animals and fish to minimize the risk to aquatic biota
(e.g. USEPA 2014).
Sulphate accumulation is a key issue in the study
area. That is detected by the lowest standards, which
represent an early level of contamination.
Nickel concentration is elevated in lakes near the
Nikel smelter and this is detected by all the standards.
However there is a great difference between different
criteria and whether water hardness is considered.
There are some unnecessarily low standards for
copper, manganese and iron. These are Russian national
maximum concentrations and considering the
background concentration they may be too sensitive
for the study area. Use of other criteria or lowest observed
toxic concentration as a reference points out
better the potential pollution in lakes downwind of the
Nikel smelter.
Eutrophication is generally not a key issue in the
study area.
28

Table 3. The lowest standards for metals with the corresponding maximum (short-term) or highest average (long-term) in certain
lakes or regions in 2012–2013 sampling period. Standard-exceeding results emphasized.
Lowest standard
(µg/l)
LN-2
(µg/l)
Kuetsjarvi
(µg/l)
Jarfjord max.
(µg/l)
Vätsäri-Russia max.
(µg/l)
Pasvik max.
(µg/l)
Al short-term 750 USEPA 134.0 113 12.0 50.0 86.0
Al long-term 87 USEPA 47.0 49.6 - 36.8 65.0
Cd short-term 0.35 USEPA 0.14 0.12 0.01 0.09 0.02
Cd long-term 0.03 CA 0.11 0.06 - 0.05 0.02
Mn short-term 10 MAHEM 123 48 6.09 13.2 (Virtuvuoshjaur) 25 (Vaggatem)
11.0 (Kochejaur)
Fe short-term 100 MAHEM 233 238 49.0 185 (Virtuvuoshjaur)
174 (Kochejaur)
365 (Vaggatem)
139 (Skrukkebukta)
Fe long-term 300 CA 143 108 - 106 223
Co short-term 10 MAHEM 6.6 2.2 0.15 0.25 0.3
Zn short-term 10 MAHEM 18 7.8 1.7 5.2 2.9
Zn long-term 8 SWE 13,65 4.7 - 2.9 1.2
Pb short-term 6 MAHEM 0.2 0.2 0.2 0.4 0.4
Table 4. Metal standard concentration by the EU priority substance list, Canadian (CCME 2011, Meays & Nordlin 2013), the US
(USEPA 2012) criteria for aquatic life, Russian MAHEM MAC values for Pasvik River, Swedish specific pollutants (Naturvårdsverket
2008) and the UK proposal for specific pollutants (UKTAG 2008). The most sensitive value for each variable bolded.
A. Long-term standards (µg/l)
Al Cd Cr (III) Cr (VI) Fe Cu Ni Zn As Se Pb Hg Cl SO4
EU EQS 0.08 3 20.00 7.2 5 0.05 5
CA 100 1 0.03 2 8.90 1.0 300 2.00 24.86 2 30.0 5 1.0 1.0 2 0.026 120 000 50 000
US 87 0.07 2 20.19 2 1.0 1000 2.01 2 11.65 2 26.7 2 150 5.0 0.3 2 230 000
SW EQS 4.00 3/8.0 4
UK 4.70 3.4 1000 1.00 6 8.0 6
B. Short-term standards and MACs (µg/l)
Al Cd Cr (III) Cr (VI) Fe Cu Ni Zn As Se Pb Hg Cl SO4
EU MAC 0.45 3 - 0.07 5
CA 640 128 000 7
US 750 0.35 2 422.42 2 16.00 2.64 2 104.78 2 26.70 2 340 8.56 2 860
RUS MAC 40 (labile) 5 70.00 20.00 100 1.00 10.00 10.00 50 2.0 6.00 0.01 300 100 000
UK 32
[1] when pH ≥ 6.5
[2] when water hardness 17 mg/l CaCO 3
(USEPA, Canada)
[3] when water hardness < 40 mg/l CaCO 3
[4] when water hardness < 24/> 24 mg/l CaCO 3
[5] lead and mercury measured with their compounds
[6] when water hardness < 50 mg/l CaCO 3
[7] when water hardness < 30 mg/l CaCO 3
29

3 Typology
Typologies and their categories have been discussed
about in the precursory work. 1 Project data from lakes
(Chapters 4 and 5) was primarily used for type categorization.
Older water quality data was used alongside
when available. The results are shown in Table 1.
Finnish typology separates North-Lapland lakes
above pine forest line as a type itself. The range of
this type was estimated by modeling from latitude, longitude
and altitude (Mikkola p.c. 2013). Based on the
geographical model three of the project lakes in area
belong to ‘North-Lapland’ type (Table 1). All the lakes
are at least 3 meters deep in average. Therefore,
based on their size (< 40 km 2 ) and water colour (< 30
mg Pt/l), the rest fit in type ‘small and medium-sized
clear water lakes’. Alkalinity threshold of 0.4 meq/l, on
average, for calcium-rich lakes was not exceeded.
Norwegian typology starts from division to altitudinal
zones. All the project lakes fall into the ‘Forest’
zone below tree line. The size category is either small
(< 5 km 2 ) or large (> 5 km 2 ). The third tier describes
alkalinity. Kuetsjarvi exceeds the average alkalinity
threshold (0.2 meq) and so represents moderately
calcium rich type. At last the lakes are categorized
for clarity based on colour (mg Pt/l) or total organic
carbon (TOC). Most lakes are clear based on water
colour values in the project data and available data
from previous years. Lake Vaggatem in the Pasvik River
watercourse is set to ‘humic’ water colour category
(colour > 30 mg Pt/l) by the Norwegian authority and
so it will be considered as such.
As a result the lakes represent two different types
according to Finnish system and four types in Norwegian
typology. Geographical location divides the
lakes in Finnish typology; size, alkalinity and colour
in the Norwegian. The resulted types describe partly
different aspects: the size category is different and
the Norwegian system has more alkalinity categories.
Water clarity is uniformly assessed and expressed by
both typologies.
The lakes closest to Nikel smelter are high in calcium.
This is mostly due to dry deposition of calcium
compounds from the industry and partly natural state
because of calcareous bedrock. Based on alkalinity
measures, however, they are, at most, moderately alkaline.
The Pasvik River is heavily modified for hydropower
and it has to a large extent lost its river character.
The large reservoirs are considered as humic lakes in
Norway. In Finnish typology these would be similarly
heavily modified or artificial humic lakes or lakes with
short retention time in case the retention is less than
10 days. Lake Vaggatem is presented as an example
here.
Photo:Esko Jaskari.
1 www.pasvikmonitoring.org
30

Table 1. Categories used in Finnish and Norwegian typologies and the resulting types for different project lakes.
Size (km 2 )
Altitude
(masl)
Calcium
(mg/l)
Alk.
(mekv/l)
Colour
(mg Pt/l)
TOC
(mg/l)
FI type NO type
FI
Lampi 222 < 5 222 < 4 < 0.2 < 30 < 5 6.1 Small and medium size clear 12 Forest zone calcium-poor clear
Harrijärvi < 5 127 < 4 < 0.2 < 30 < 5 6.1 Small and medium size clear 12 Forest zone calcium-poor clear
Pitkä-Surnujärvi < 5 126 < 4 < 0.2 < 30 < 5 6.1 Small and medium size clear 12 Forest zone calcium-poor clear
Sierramjärvi < 5 254 < 4 < 0.2 < 30 < 5 1 North-Lapland lakes 12 Forest zone calcium-poor clear
RU
Shuonijaur 5–40 180 < 4 < 0.2 < 30 < 5 6.1 Small and medium size clear 12 Forest zone large calcium-poor clear
Ala-Nautsijarvi 5–40 159 < 4 < 0.2 < 30 5–15 6.1 Small and medium size clear 12 Forest zone large calcium-poor clear
Toartesjaur < 5 195 < 4 < 0.2 < 30 < 5 6.1 Small and medium size clear 12 Forest zone calcium-poor clear
Virtuovoshjaur < 5 182 < 4 < 0.2 < 30 5-15 6.1 Small and medium size clear 12 Forest zone calcium-poor clear
Riuttikjaure < 5 190 < 4 < 0.2 < 30 < 5 6.1 Small and medium size clear 12 Forest zone calcium-poor clear
Kochejaur < 5 133 < 4 < 0.2 < 30 5–15 6.1 Small and medium size clear 12 Forest zone calcium-poor clear
LN-2 < 5 210 > 20 < 0.2 < 30 < 5 6.1 Small and medium size clear * 12 Forest zone calcium-poor clear
Kuetsjarvi 5–40 21 4–20 0.2-1 < 30 5–15 6.1 Small and medium size clear 14.7 Forest zone large moderately calcium-rich
NO
Gardsjøen < 5 82 < 4 < 0.2 < 30 < 5 6.1 Small and medium size clear 12 Forest zone calcium-poor clear
Holmvatnet < 5 156 < 4 < 0.2 < 30 < 5 1 North-Lapland lakes 12 Forest zone calcium-poor clear
Rabbvatnet < 5 83 < 4 < 0.2 < 30 < 5 6.1 Small and medium size clear 12 Forest zone calcium-poor clear
Durvatn < 5 231 < 4 < 0.2 < 30 < 5 1 North-Lapland lakes 12 Forest zone calcium-poor clear
Børsevatn < 5 178 < 4 < 0.2 < 30 < 5 6.1 Small and medium size clear * 12 Forest zone calcium-poor clear
Vaggatem 5–40 51 < 4 < 0.2 30–90 5–15 7.1. Medium-sized humic lakes 13 Forest zone large calcium-poor humic lakes
*borderline case on the modeled forest line
31

4 Phytoplankton
Data processed here consist of phytoplankton densities
and chlorophyll a contents for Vätsäri and Russian
lakes (Chapter 4). The sampled zone for phytoplankton
was 0–2 m in Finland and 0–10 m in Russia
and Norway. Sampling was conducted during growing
season in June–September. For detailed sampling
methods see Chapter 4 Biology.
Results and discussion
Chlorophyll
The measured chlorophyll a was low in Vätsäri lakes:
less than 2 µg/l (Table 1). The reliable detection limit
in the analysis is 1 µg/l. The studied lakes in Russia
had similarly low chlorophyll content, apart from Lake
Shuonijaur. Lake chlorophyll a is a phytoplankton metric
in Finnish, Norwegian and Swedish classification.
All systems give consistent good or high status class
to measured chlorophyll results. The Norwegian reference
value for northern clear water lakes is the lowest,
thus it would be the first metric to react to a rise
in chlorophyll content.
Species diversity
Even though diversity indices are not used for phytoplankton
classification in any country the number of
species between the regions is compared in Figure 1.
Norwegian Jarfjord and Finnish Vätsäri are neighbouring
regions with rather similar trophic status. Some
of the Jarfjord lakes have very high copper content
(see Chapter 4) and therefore the species diversity
is expected to be lower than in Vätsäri. Russian lakes
locate more south and are mostly bigger in surface
and catchment area, which should contribute to
higher phytoplankton species diversity.
The Russian lakes differ from the other two regions
with significantly higher number of species (Kruskal-
Wallis, p = 0.01). Jarfjord lakes tend to have fewer
species, but in this data set no difference to Vätsäri
could be verified.
Conclusions
Growing season chlorophyll a content gives an indication
of the phytoplankton biomass and thus of
the general trophic status. Applying the limit values
for correct type in any of the national classifications
should reveal possible status deterioration. Therefore
chlorophyll content should be measured systematically
from all the locations.
Biomass of harmful cyanobacteria (blue-green
algae) is a straightforward and commonly used
phytoplankton metric of eutrophication for which taxa
biomasses would be required. It would be a good additional
tool for assessing phytoplankton communities.
Phytoplankton seasonal changes should be considered
when using it as a quality element. Phytoplankton
classification is recommended to be based
on more than one growing season sample. In Norway
five annual samples are recommended (Direktoratsgruppa
2013).
32

Table 1. Average measured chlorophyll in June–September 2013 and the current type-specific reference values,
high/good (H/G) class limit values and consequent status classes in Finnish, Norwegian and Swedish lake
classification.
Vätsäri
Chlorophyll a
(µg/l)
Harrijärvi 1.65
Lampi 222 < 1
Pitkä-Surnujärvi 1.25
Finnish 1 Norwegian 2 Swedish 3
Ref.
(µg/l)
H/G
(µg/l)
3 4
Sierramjärvi < 1 2 3
Russia
Shuonijaur 2.44
Ala-Nautsijarvi 0.78
Toartesjaur 1.44
Riuttikjaure 0.41
Virtuovoshjaur 0.6
Kochejaur 1.05
class
Ref.
(µg/l)
H/G
(µg/l)
class
Ref.
(µg/l)
H/G
(µg/l)
class
high 1.3 2 high 2 4 high
3 4 high 1.3 2
good
high
2 4 high
[1] Finnish types: small and medium size clear lakes or (Sierramjärvi) North-Lapland lakes.
[2] All lakes forest zone calcium-poor clear water (L-N5) type
[3] Ecotype for phytoplankton: Northland clear lakes.
Gardsjøen
Holmvatnet
Rabbvatnet
Durvatn
average
0 10 20 30 40 50 60
6
4
5
7
5,5
Lampi 222
Harrijärvi
Pitkä-Surnujärvi
Sierramjärvi
average
6
12
13
12
17
Shuonijaur
Ala-Nautsijarvi
Toartesjaur
Virtuovoshjaur
Riuttikjaure
Kochejaur
average
11
16
20
19
31
24,5
50
Jarfjord Vätsäri Russia
Figure 1. The number of phytoplankton species in each lake and the regional
averages.
33

5 Periphytic diatoms
Diatom communities have been traditionally studied
in rivers and there are only few metrics that apply to
lake littoral habitats. Here diatom communities were
assessed using Diatom Assessment of Lake Ecological
Status (DALES) for nutrient level, saprobity index
for organic pollution and community metrics for general
quality. The material includes Chapter 4 lake data.
Results and discussion
Diversity
Diatom species diversity and their relative abundances
were estimated with Shannon entropy (Shannon
& Weaver 1962) (Table 1). The index value increases
with both species number and growing equality in
abundance. Evenness value is derived from the index
to illustrate how evenly the species are distributed.
The results between the three regions were compared
using a non-parametric Kruskal-Wallis test.
Species diversity is used in ecological assessment,
but they are not among the northern Water Framework
Directive classification tools. Based on water
quality analysis (See Chapter 4, Water quality), Vätsäri
lakes represent the most undisturbed small lakes
and Jarfjord lakes represent slightly polluted, small,
clear water lakes in the same region. Pollution impact
was expected to show in lower diversity in Jarfjord.
Due to their more southern location and greater size
the Russian lakes are expected represent ecologically
divergent lake types with more species.
Species richness and diversity in the diatom
samples was the highest in Vätsäri area and lowest
in Jarfjord, with regional average of 60 and 28 spp.
respectively. The difference is statistically significant
(Kruskal-Wallis, p = 0.05). The number of species in
Russian lakes varied widely from 22 to 56, being 40
Table 1. Number of diatom species, Shannon entropy and evenness
value for each lake calculated from diatom species. For
Vätsäri lakes values are averages of 2–3 sampling stations.
No. of species Shannon Evenness
Vätsäri
Lampi 222 50.7 2.98 0.76
Harrijärvi 37.5 2.51 0.69
Pitkä-Surnujärvi 83.3 3.54 0.8
Sierramjärvi 67.0 3.08 0.73
Average 59.6 3.00 0.75
Russia
Shuonijaur 22 1.54 0.5
Ala-Nautsijarvi 30 3.03 0.89
Toartesjaur 46 3.18 0.83
Virtuovoshjaur 46 2.82 0.74
Riuttikjaure 56 3.58 0.89
Average 40.0 2.83 0.77
Jarfjord
Gardsjøen 23 2.08 0.66
Holmvatnet 22 2.34 0.76
Rabbvatnet 31 2.7 0.79
Durvatn 34 2.97 0.84
Average 27.5 2.5 0.76
34

on average. Shannon entropy or evenness was not
significantly different between the three areas.
Diatom Assessment of Lake Ecological status
DALES index was developed in the UK for lake diatom
community assessment. It is an average score per
taxa method in terms of eutrophication impact. The
sample taxa were mostly found among the UKTAG
(2008) list of species or genuses. In case species level
was not found the genus alone was used. Genera
that weren’t listed were ignored in calculation.
The reference index value for low alkalinity water
(0.8) (Table
2). The index values somewhat reflected the water
quality: the values were highest in the southern lakes,
which are more humic and higher in nutrients. The difference
was due to higher proportion of Nitzschia and
certain Gomphonema and Navicula species. Fragilaria
on genus level gives high score compared to some
individual species in the genus, and so having many
Fragilaria sp. in the data also raises the index value.
Saprobic index
Sladecek (1973) saprobic index resulted oligosaprobic
level and clean water quality class for majority of
lakes (Table 3). Shuonijaur diatom community was
found to be mesosaprobic and it exceeded the Russian
standard limit for clean water class with two other
lakes.
Table 2. DALES index values, their respective ecological quality
ratios (EQR) and mean 2012-2013 total phosphorus content
for each lake. Against the reference value (20) and high/good
threshold (36), all results represent high status.
DALES Class P tot. mean (µg/l)
Vätsäri
Lampi 222 14
2.0
Harrijärvi 6 2.3
high
Pitkä-Surnujärvi 20 4.0
Sierramjärvi 26 3.8
Russia
Shuonijaur 26
4.8
Ala-Nautsijarvi 27 3.0
Toartesjaur 28 high
7.5
Virtuovoshjaur 21 8.5
Riuttikjaure 36 7.0
Jarfjord
Gardsjøen 20
Holmvatnet 12
Rabbvatnet 19
high
Durvatn 23
Table 3. Diatom periphyton inferred water quality estimation by
the saprobic index (S), the consequent saprobic level and the
Russian standard water quality class (II clean; III moderately
polluted).
S Saprobic level Class
Vätsäri
Lampi 222 1.16 α-oligosaprobic II
Harrijärvi 1.25 α-oligosaprobic II
Pitkä Surnujärvi 1.22 α-oligosaprobic II
Sierramjärvi 1.44 α-oligosaprobic II
Russia
Shuonijaur 1.63 β-mesosaprobic III
Ala-Nautsijarvi 1.57 α-oligosaprobic III
Toartesjaur 1.46 α-oligosaprobic II
Virtuovoshjaur 1.39 α-oligosaprobic II
Riuttikjaure 1.42 α-oligosaprobic II
Jarfjord
Gardsjoen 1.55 α-oligosaprobic III
Holmvatn 1.34 α-oligosaprobic II
Rabbvatn 1.45 α-oligosaprobic II
Durvatn 1.22 α-oligosaprobic II
35

pH reconstruction by diatom analysis
Diatom communities can be used in estimation of their
environment pH with method by Moiseenko & Razumovskii
(2009). The results come close to measured
pH values which represent neutral water (Table 4).
This supports the assumption that recently measured
pH values represent the prevailing long-term level in
the lakes.
Community indices
Finnish classification of type-specific species and percent
model affinity give variable results (Table 5). The
indices are currently based on rather small reference
lake data which most likely explains the moderately
low results: the studied lakes are not affected by any
large scale impact and therefore the results should indicate
at least good status class. The larger lakes (>
5 km) were not classified for the lack of representative
reference data.
Conclusions
DALES index seemed to have an approximate reaction
to nutrient level. Index taxa did not perfectly match
the northern taxonomic composition and some species
had to be dropped out or listed on genus level
which caused unwanted variation to index values.
Saprobic index gave reasonable results in primarily
pointing out the one mesosaprobic lake. Lakes in Vätsäri
and Jarfjord were found to be oligosaprobic, as
expected. Diatom community as pH estimator proved
to be an accurate indicator of cation-anion balance.
The data was scarce both spatially and temporally,
consisting of only 4–5 lakes per area and only one
diatom sample from most of them. There were also
some differences in the sampling efforts between the
regions and also in the regional catchment properties
that were not controlled in the study. All this may contribute
to the observed diversity results. Nevertheless,
changes in species richness would be a straightforward
ecological metric easy to monitor in the future.
Table 4. The diatom-inferred pH and chemical measured pH for
the investigated lakes. Vätsäri lake values are averages from
three parallel samples.
Table 5. Average ecological quality ratio (EQR) of the Finnish
type-specific species and percent model affinity metrics. Results
vary from 0.4 < moderate > 0.6 to 0.6 < good > 0.8.
Lake pH (diatom) pH (chemical)
FI type
Class (EQR)
Vätsäri
Lampi 222 6.8 6.7
Harrijärvi 6.7 6.7
Pitkä Surnujärvi 6.9 6.7
Sierramjärvi 7.0 6.8
Russia
Shuonijaut 6.8 6.8
Ala-Nautsijarvi 7.3 7.0
Toartesjaur 7.0 6.9
Virtuovoshjaur 7.0 6.9
Riuttikjaure 7.2 7.1
Jarfjord
Gardsjoen 6.9 6.8
Holmvatn 6.9 6.8
Rabbvatn 7.0 7.0
Durvatn 7.0 7.0
Vätsäri
Lampi 222 6.1 good (0.6)
Harrijärvi 6.1 good (0.6)
Pitkä-Surnujärvi 6.1 good (0.6)
Sierramjärvi 1 good (0.6)
Russia
Shuonijaur - -
Ala-Nautsijarvi - -
Toartesjaur 6.1 good (0.6)
Virtuovoshjaur 6.1 moderate (0.5)
Riuttikjaure 6.1 good (0.6)
Jarfjord
Gardsjøen 6.1 good (0.6)
Holmvatnet 1 moderate (0.5)
Rabvatn 6.1 moderate (0.4)
Durvatn 1 moderate (0.5)
36

6 Zoobenthos
Benthic macroinvertebrate metrics for biological classification
are studied here as they are considered in
the Finnish, Norwegian and Swedish classifications
and Russian standards. Finnish and Swedish indices
are primarily community indices. The Norwegian approach
is to detect the main impact and use relevant
metrics. Here acidification and chemical pollution are
considered the principal impacts. Swedish and Norwegian
systems apply additional multimetric indices
that were not possible to calculate by hand.
In addition to the small lakes zoobenthos data from
lakes in the Pasvik River was collected. Lake Kuetsjarvi
and Vaggatem results are included here to create
a gradient in terms of industrial pollution.
Littoral data was collected using a kick-net (mesh
size 0.5 mm) with total of 1.5–2 minutes of sampling.
Profundal samples are collected with 2–5 Ekman grab
samples. Data sampling details are available in the
publication Ylikörkkö et al. (2015).
Results and discussion
Community metrics
The Finnish littoral indices, type-specific species and
percent model affinity (PMA), lack reference data for
clear northern lakes where taxa is scarce. The indices
use more southern data as comparison, resulting in
falsely poor and bad values for both littoral indices.
For this reason the littoral results will not be considered
further.
The profundal samples had very few individuals,
especially in Jarfjord and Vätsäri. The above mentioned
weakness was also apparent in profundal PMA,
which yielded low results (Table 1). Some lakes had
so few taxa that PMA would have been 0, which is
not a useful result. Profundal Invertebrate Community
Metric (PICM) requires certain indicator Chironomidae
and Oligochaeta on species level to assess benthos
status. PICM species were not found in half of the lakes.
This is likely because of extremely low density
of invertebrates in the studied lakes and not because
of true oxygen depletion. When there were indicator
species for PICM calculation, the presence of relatively
sensitive Sergentia and Cladotanytarsus resulted
in high index values and high status class (Table 1).
Swedish average score per taxon (ASPT) method
measures littoral ecological quality through taxa indicator
values in relation to pollution (Naturvårdsverket
2007). The index value is an average of all indicator
taxa. It also describes the community diversity,
namely the proportion of resilient Chironomidae and
Oligochaeta to the more sensitive mayflies (Ephemeroptera),
caddisflies (Trichoptera), stoneflies (Plecoptera),
etc. The higher the score the more sensitive
species it holds. The Swedish indices use regional
reference states based on Illies’ ecoregions and the
studied lakes belong to ‘boreal upland’.
The results from APST vary from moderate to high
(Table 2). There is no evident explanation for moderate
results, other than low distribution of fauna. There
is a wide variance in the results even in the reference
lakes, which shows in lowered class boundaries. Likely
the reference data does not suit well the northernmost
lakes.
Benthic quality index (BQI) is the Swedish profundal
metric. The index is an average score per taxon
method for Chironomidae species in terms of their tolerance
to oxygen depletion.
For most lakes there were no indicator species
identified and thus no material to calculate the index
from. There was mostly one or at most two indicator
species in profundal samples that enabled index
calculation. Results indicate mainly good status class
(Table 3). In Harrijärvi the presence of Chironomus
plumosus dropped the status to poor. As there were
very few taxa small differences in the community reflected
as drastic changes in the index value. In addition,
the class limit values were set higher than usually,
requiring more than 70 % of the reference value
to reach good status.
37

Table 1. The Finnish lake type, profundal PMA and PICM status classes. The index could not
be used if indicator taxa for PICM calculation was not present.
FI Type PMA PICM Final status
Vätsäri
Lampi222 6.1 - no taxa -
Harrijärvi 6.1 Good Good Good
Pitkä-Surnujärvi 6.1 Good no taxa Good
Sierramjärvi 1 - High High
Russia
Shuonijaur 6.1 High High High
Ala-Nautsijarvi 6.1 Good High High
Toartesjaur 6.1 Moderate High High
Virtuovoshjaur 6.1 - no taxa
Riuttikjaur 6.1 Poor High High
Kochejaur 6.1 - no taxa -
the Pasvik River
Vaggatem 7.1 - no taxa -
Kuetsjarvi 6.1 Good High High
Jarfjord
Gardsjøen 6.1 High High High
Holmvatnet 1 - no taxa -
Rabbvatnet 6.1 Moderate High High
Durvatn 1 - no taxa -
Table 2. The average score per taxon (ASPT) value and the
corresponding status class. Reference value for ‘boreal upland’
is 5.6. The status class boundaries are set lower than usually:
high/good threshold 3.36 (60 % of the reference).
ASPT
Class
Vätsäri
Lampi222 3.5 High
Harrijärvi 1.9 Moderate
Pitkä-Surnujärvi 2.5 Moderate
Sierramjärvi 2.8 Good
Russia
Shuonijaur 4.4 Good
Ala-Nautsijarvi 3 Good
Toartesjaur 2.9 Good
Virtuovoshjaur 4.8 High
Riuttikjaure 5.7 High
Kochejaur 4.4 Good
the Pasvik River
Vaggatem 2.9 Good
Kuetsjarvi 2.7 Good
Jarfjord
Gardsjøen 2.1 Moderate
Holmvatnet 5.3 High
Rabbvatnet 2.7 Good
Durvatn 6.1 High
Table 3. Benthic quality index (BQI) value and the corresponding
status class. The reference value for ‘boreal upland’ is
3.25. The class limit values are set higher than usually: high/
good threshold 3.1, good/moderate threshold 2.28 (70 % of the
reference).
Vätsäri
BQI
Class
Harrijärvi 1 Poor
Sierramjärvi 3 Good
Russia and the Pasvik River
Shuonijaur 3 Good
Kuetsjarvi 3 Good
Jarfjord
Gardsjøen 3 Good
Holmvatnet 3 Good
38

Index for acidification
Norwegian ‘Raddum’ index for acidification measures
the presence of indicator taxa in the littoral zoobenthos
community (Direktoratsgruppa 2009). It is
based on a list of indicator values from 0 to 1 for certain
taxa. Observed taxa are given scores and simply
the highest score i.e. the most sensitive species makes
the index value. Taxa that were not found in reference
list were excluded.
The index results vary from no impact (1) to moderately
(0.5) and strongly acidified (0.25) (Table 4).
There were three lakes where the only indicator species
were tolerant and so the results indicate severe
acidification (0). However, water quality in any of the
lakes does not support noticeable acidification. The
difference between index values 1 and 0 was made in
many cases by a single species, e.g. just one individual
of Gammarus in the sample. Evidently the index
was worsened by naturally low diversity and density of
benthic fauna. The index does show consistently low
values in Jarfjord, where acidification has been observed
in other lakes (Puro-Tahvanainen et al. 2011;
Chapter 4 Water quality). So far it cannot be ruled out
that benthic communities in the studied lakes haven’t
suffered from acidic deposition.
According to the Norwegian classification the
amphipod Gammarus species are used as an indicator
of good status class (Direktoratsgruppa 2009).
They are considered sensitive especially to acidification.
In the studied lakes Gammarus lacustris was
found in lakes Sierramjärvi and Lampi 222 in Vätsäri.
Index for organic pollution
Woodiwiss index, or so called ‘Trend Biotic Index’
(Woodiwiss 1964), is a standard tool in Russia to
analyse littoral zoobenthos communities in terms
of organic pollution impact. The index is based on
the presence or absence of key taxonomic groups:
Ephemeroptera, Plecoptera, Trichopera, Asellus sp.
etc. Taxa abundances are not considered. The index
value decreases from 10 to 0 towards pollution.
The results indicate mainly clean (>7) status (Table
5). Shuonijaur, Kuetsjarvi and Vaggatem yielded
slightly deteriorated values (6–7). The result is sensible
as these lakes lie closest to pollution sources of
the Pechenganikel. Certain Jarfjord lakes yield the lowest
values (3–4), which is a result of fewer key taxa.
Table 4. The Raddum index for acidification values for the
studied lakes. 1 indicates no acidification, 0.5 moderate, 0.25
strong and 0 the most severe acidification.
Raddum value
Vätsäri
Lampi222 1
Harrijärvi 0
Pitkä-Surnujärvi 1
Sierramjärvi 1
Russia
Shuonijaur 1
Ala-Nautsijarvi 1
Toartesjaur 1
Virtuovoshjaur 1
Riuttikjaure 1
Kochejaur 1
the Pasvik River
Vaggatem 1
Kuetsjarvi 1
Jarfjord
Gardsjøen 0.25
Holmvatnet 0
Rabbvatnet 0.5
Durvatn 0
Table 5. The Woodiwiss/Trend Biotic Index and the corresponding
status for the studied lakes littoral data (I: clean, II: clean,
III: moderately polluted).
Woodiwiss
Status
Vätsäri
Lampi222 9 I
Harrijärvi 9 I
Pitkä-Surnujärvi 9 I
Sierramjärvi 9 I
Russia
Shuonijaur 7 II
Ala-Nautsijarvi 8 I
Toartesjaur 8 I
Virtuovoshjaur 10 I
Riuttikjaure 8 I
Kochejaur 10 I
the Pasvik River
Vaggatem 7 II
Kuetsjarvi 6–7 II
Jarfjord
Gardsjøen 3 III
Holmvatnet 4 III
Rabbvatnet 8 I
Durvatn 6 II
39

Table 6. The number of families and the number of EPT families
in the studied lakes’ littoral data.
Families
EPT-families
Vätsäri
Lampi 222 11 2
Harrijärvi 10 4
Pitkä-Surnujärvi 12 5
Sierramjärvi 16 6
Average 12.3 4.3
Russia
Shuonijaur 8 2
Ala-Nautsijarvi 11 3
Toartesjaur 10 5
Virtuovoshjaur 13 6
Riuttikjaure 10 5
Kochejaur 18 9
Average 11.7 5
the Pasvik River
Vaggatem 6 0
Kuetsjarvi 11 5
Jarfjord
Gardsjøen 4 0
Holmvatnet 3 1
Rabbvatnet 8 5
Durvatn 9 4
Average 6.0 2.5
Number of families and EPT-families
Number of families in littoral and riparian zones is a
metric in the Canadian zoobenthos assessment. The
number of Ephemeroptera, Plecoptera and Trichoptera
(EPT) families together is a widely used parameter
in Canada and also as part of Swedish multimetric
zoobenthos index ‘MILA’. The parameters are only
used as a part of greater entity or multimetric index
and there are no reference values available. Nevertheless,
the number of families and the number of EPT
families in littoral samples are studied here. Expectations
were similar to those for periphyton diversity:
pollution-affected Jarfjord area ought to express lower
family diversities.
The only notable difference in family diversity between
the areas was indeed lower number of all families
in Jarfjord (Table 6). On average there were 6
families, in comparison to 12 families both in Vätsäri
and the Russian lakes. The difference was statistically
close to significant (p=0.06, Kruskal-Wallis). In Jarfjord
the number of EPT families was also lower, but
there was no statistical significance between the areas
(p=0.38, Kruskal-Wallis).
In the Pasvik River littoral family diversity was rather
low in Vaggatem. The location is prone to several
factors that add up to the result, including water level
regulation and slight pollution. Effects of water level
regulation on littoral benthos have been studied in Keto
et al. (2008), where the decline in Ephemeroptera
and Trichoptera taxa were observed to result from
regulation. There were no EPT-families found in the
2013 samples in Vaggatem. Moderately polluted Lake
Kuetsjarvi expressed fairly average results. However,
there are more nutrients in Kuetsjarvi (See Chapter 3
Water quality) than in the other small lakes and thus
it’s not completely comparable to them.
Conclusions
Many of the mathematical indices proved to be prone
to falsely low results due to low abundance of benthic
fauna. This shows in large-scale drop in status with
loss of just one species. At the moment, many of the
community indices are too uncertain to be applied in
status assessment.
Ephemeroptera, Plecoptera and Trichoptera species
are considered sensitive to acidification, pollution
and water level regulation. The number of families and
EPT families are feasible diversity metrics that could
be used in detecting changes in time, presuming the
sampling efforts are standardized. Similarly the Woodiwiss/Trend
Biotic Index pointed out the communities
lacking EPT indicator species.
40

References
CCME. 2012: Canadian Environmental Guidelines. Canadian Council of Ministers of the Environment. http://ceqg-rcqe.
ccme.ca/ [accessed 1.11.2012].
Direktoratsgruppa Vanndirektivet. 2009: Klassifisering av miljøtilstand i vann. Veileder 01:2009. (in Norwegian)
Direktoratsgruppa Vanndirektivet. 2013 Klassifisering av miljøtilstand i vann. Veileder 02:2013. 181 p. (in Norwegian)
Keto, A., Sutela, T., Aroviita, J., Tarvainen, A., Hämäläinen, H., Hellsten, S., Vehanen, T., Marttunen, M. 2008: Säännösteltyjen
järvien ekologisen tilan arviointi. Suomen ympäristö 41/2008. 105 p. (in Finnish)
Naturvårdsverket. 2007: Status, potential och kvalitetskrav för sjöar, vattendrag, kustvatten och vatten i övergångszon. En
handbok om hur kvalitetskrav i ytvattenförekomster kan bestämmas och följas upp. Naturvårdsverket. 2007:4. 66 p. (in
Swedish)
Naturvårdsverket. 2008: Förslag till gränsvärden för särskilda förorenande ämnen. Stöd till vattenmyndigheterna vid statusklassificering
och fastställande av MKN. Swedish Environmental Protection Agency. Report 5799 (in Swedish)
Meays, C. & Nordlin, R. 2013. Ambient Water Quality Guidelines For Sulphate. Update April 2013. Ministry of Environment
Province of British Columbia. 55 p. http://www.env.gov.bc.ca/wat/wq/BCguidelines/sulphate/pdf/sulphate_final_guideline.
pdf
Mikkola, K. (FM) 2013: Finnish Forest Research Institute. Personal communication.
Salminen R. (ed.) 2005: Geochemical atlas of Europe. Part 1 Background information, methodology and maps. http://weppi.
gtk.fi/publ/foregsatlas/ [accessed 3.3.2014]
Shannon, C.E., Weaver, W. 1963: The mathematical theory of communication. University of Illinois Press, Urbana. 117 p.
Sladecek, V. 1973: System of water quality from biological point of view. Archiv für Hydrobiologie – Beiheft Ergebnisse der
Limnologie 7: 1–128.
State of Alaska. 2012: Alaska Water Quality Criteria Manual for Toxic and Other Deleterious Organic and Inorganic Substances.
Department of Environmental conservation. 45 p.
UKTAG. 2008: Proposals for environmental quality standards for VIII substances. UK Technical Advisory Group on the
Water Framework Directive. 92 p.
UKTAG. 2008: UKTAG lake assessment methods macrophytes and phytobenthos, phytobenthos – diatom assessment of
lake ecological quality (DARLEQ). Water Framework Directive - United Kingdom Advisory Group. 19 p. http://www.wfduk.
org/sites/default/files/Media/Characterisation%20of%20the%20water%20environment/Biological%20Method%20Statements/Lake%20phytobenthos.pdf
USEPA. 2010: Final Report on Acute and Chronic Toxicity of Nitrate, Nitrite, Boron, Manganese, Fluoride, Chloride and
Sulfate to Several Aquatic Animal Species. http://www.epa.gov/r5water/wqs5/pdfs/techdocs/FINAL%20Report%20EPA-
905-R-10-002.pdf [accessed 3.3.2014]
USEPA 2012: Water quality criteria. Aquatic life criteria. United States Environmental Protection Agency. http://water.epa.
gov/scitech/swguidance/standards/criteria/aqlife/index.cfm [accessed 5.11.2012]
Ylikörkkö, J., Christensen, G.N., Andersen, H.J., Denisov, D., Amundsen, P.-A., Terentjev, P., Jelkänen, E. (edit.) 2015: Environmental
Monitoring Programme for Aquatic Ecosystems in the Norwegian, Finnish and Russian Border Area – Updated
Implementation Guidelines
Fixing oxygen samples.
Photo: Esko Jaskari
41

42

Chapter 3: The ecological condition of the
Pasvik River and Lake Inarijärvi
Macrophyte studies in Lake Muddusjärvi. Photo: Jukka Ylikörkkö
43

1 Introduction
The areas of interest in Chapter 3 were Lake Inarijärvi,
the Pasvik River and the major lakes in the Pasvik
watercourse. The ecological status of Lake Inarijärvi
and the Pasvik River were estimated by using defined
indicators sensitive for hydrological change induced
by climate change and water level regulation. The
presence and amounts of contaminants in the water,
fish tissues and bottom sediments of the watercourse
were determined. The effects of climate change,
contaminants, water level regulation and species
introductions and invasions in the Pasvik River were
estimated by analyzing a long time series of fish population
in two main lakes.
The water quality in the Pasvik area has been monitored
for longer than biological indicators of ecological
status. The newest trends in water quality have
been published in a separate report Pasvik Water
Quality until 2013 (Ylikörkkö et al. 2014) which is a
continuation of earlier reports of 2007 and 2011.
The main reason for the monitoring of the water
quality in the Pasvik watercourse is the Pechenganikel
copper-nickel smelter complex on the Kola Peninsula.
Lake Inarijärvi and the upper part of the Pasvik
River are affected mainly by low levels of atmospherically
transported pollutants whereas the lower part in
the vicinity of the combine and the areas downstream
are affected by both direct waste water discharge and
atmospheric deposition. Along the Pasvik watercourse
there is also discharge of water from the Pasvik
hydropower plant cascade. The issues tied to the hydropower
stations include changes in the rivers’ regime
resultant from construction of water reservoirs, upsetting
of the natural water balance and impact on the
hydro-chemical regime of small rivers and their selfcleaning
capacity.
Lake Inarijärvi is situated in the upstream of the
Pasvik watercourse and is free of direct emissions
from the Pechenganikel. There is some nutrient loading
from diffuse and point sources and the Pasvik
River regulation inflicts moderate water level fluctuation.
The water quality is excellent and the measured
chemical indicators have stayed on the same levels
throughout the monitoring programme.
In the Pasvik River and the directly connected lakes
the water monitoring data of the observation period
confirms certain stabilization of the established
hydro-chemical regime, pollutants’ concentrations and
pollution indicators. Copper, nickel, and sulphates are
the main pollutants of the basin. The monitoring data
also shows that pollutants are brought into the water
bodies of the river basin system both directly with
wastewater and by way of long-range transboundary
air transport. The most polluted water body in the
basin is the Kolosjoki River where the Pechenganikel
combine plant wastewater is discharged, as well
as the stream connecting the Lakes Salmijarvi and
Kuetsjarvi. The concentration of metals and sulphates
in the water notably increases downstream from
Lake Kuetsjarvi. In Lake Kuetsjarvi the concentrations
of copper and nickel are clearly elevated and have
changed insignificantly in the last years of the monitoring
period
44

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Muddusjärvi
Inari
!(
Nitsijärvi
!(
INARIJÄRVI
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!(
!(
!(
Näätämö
!(
!(
Vaggatem
Tjerebukta
Ruskebukta
Rajakoski
Hestefoss
!(
")
!(
River Pasvik
")
!( ")
")
!(
!(
!(
!(
Fuglebukta
Langvatnet
")
Kirkenes
")
!(
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!(
!(
Skrukkebukta !(
Björnevatn
Svanevatn
!(
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Kuetsjarvi
!(
NIKEL
!(
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Pechenga
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ZAPOLYARNY
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Figure 1. Map of the study area.
!(
!(
0 50
km
© Maanmittauslaitos, lupa nro 7/MML/14
!(
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Golfstream
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Beliy kamen
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Salmijarvi
Kolosjoki
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km
Figure 2. Map of the monitoring stations of Lake Kuetsjarvi.
© Karttakeskus Oy, Lupa L4659
45

2 Climate change impacts on hydrology
and water level fluctuation
SEPPO HELLSTEN AND JUHA RIIHIMÄKI
Climate change forecasts used in this study (see generally
Veijalainen et al. 2012) estimate annual mean
temperature in Lake Inarijärvi watershed to increase
3.6 °C in 2040–2069 compared to annual mean temperature
during 1971–2000 and increase on mean
winter temperature (December–February) is estimated
to be 5.1 °C. Annual mean precipitation is estimated
to increase 12 % and winter mean precipitation 16
%. These changes would affect also the yearly hydrological
cycle altering timing of high and low water
levels and discharges on lakes and rivers.
The hydrological model, Watershed Simulation and
Forecasting System (WSFS) (Vehviläinen et al. 2005),
was used to estimate climate change impacts on hydrology
of the Pasvik River catchment (Figure 1). Climate
chance scenario used was mean of 19 global
climate models calculated by Finnish meteorological
institute FMI (Jylhä et al. 2009) with emission scenario
SRES A1B (IPCC 2000).
Effects of climate change induced changes on
the Pasvik River hydrology and Lake Inarijärvi water
levels were analyzed using DHRAM calculation programme
on the Pasvik River and water-level fluctuation
analysis tool (Regcel) on Lake Inarijärvi. Scenario
period used in this study was 2040–2069 and reference
period used was 1971–2000.
Materials and methods
Regcel model developed in the Finnish Environment
Institute (SYKE) was used to enable assessment of
the ecological impacts of water-level regulation on
aquatic macrophytes, benthic invertebrates, fishes
and nesting of waterfowl. Indicators for Lake Inarijärvi
were calculated for reference period 1971–2000 and
the average values of that period were compared to
indicators calculated for the scenario period 2040–
2069.
The Dundee Hydrological Regime Assessment
Method (DHRAM) was used for water flow analysis.
This approach compares differences between the
affected and unaffected flow data and is therefore
descriptive. It resembles the Regcel application, but
uses only discharge data without any measured biological
response. The method assessess the degree
of hydrological alteration, presuming that the change
is having an ecologically harmful impact on naturally
adapted biota. Discharge factors are divided into
five different groups, in which both mean value (A)
and coefficients of variation (B) are used as indicators.
Comparisons between un-affected and affected
situations are calculated as an absolute change (%).
Hydrological change thresholds used for allocation of
Water level (m.a.s.l.)
119,6
119,4
119,2
119,0
118,8
118,6
118,4
118,2
118,0
117,8
1.1. 1.2. 1.3. 1.4. 1.5. 1.6. 1.7. 1.8. 1.9. 1.10. 1.11.1.12.
Date
MW 1971-2000 MW 2040-2069
Discharge (m 3 /s)
240
0
1.1. 1.2. 1.3. 1.4. 1.5. 1.6. 1.7. 1.8. 1.9. 1.10. 1.11. 1.12.
Date
MQ 1971-2000 MQ 2040-2069
Figure 1. Simulated water level fluctuations (left) and simulated discharges (right) for the reference period and the scenario period at Lake Inarijärvi.
200
160
120
80
40
46

final impact points and the final impact classes can be
found in Black et al. (2000).
In our study DHRAM was applied to compare the
regulated water flow (reference period) in Kaitakoski
(1970–2000) and simulated flow for climate change
scenario (2040–2069).
Results
Regcel
Results for the water level fluctuation indicators of
Lake Inarijärvi are presented on Table 1. Simulated
water levels have quite clear influence in some of the
indicators although there are many indicators where
there is no change or it is negligible.
Magnitude of spring flood is an indicator that describes
“cleaning effect” of spring high water levels
transporting the dead organic material to upper shore
areas. Higher spring flood is considered to inhibit
excessive growth of shore vegetation. Spring flood
magnitude in Lake Inarijärvi is very small in reference
period and there is no spring flood at all in scenario
period.
Water level change during growing season in Lake
Inarijärvi is smaller in scenario period than in reference
period. The change is calculated by subtracting
75 % fractal of water levels of the first ice-free month
from the 75 % fractal of water levels of the rest of the
growing season (from 30 days after ice-out to 30th
September). If the indicator value of the water level
change were negative (water level is lowering during
the growing season), it would promote zonation of
littoral vegetation and would have positive effect on
shore habitat diversity. However, the indicator value is
positive (water level is rising during the growing season)
in both reference period and scenario period indicating
unfavorable conditions. Nevertheless, the indicator
value is smaller (and better) in scenario period.”
Carex zone is the optimum growing level of sedge
(Carex) species being important habitat for northern
pike spawning. Change in maximum vertical extension
of Carex zone and decrease of water level during
spawning of northern pike is negligible. However,
although indicator minimum water depth in the Carex
zone during the spawning of northern pike is having
negative value indicating that water levels at Lake Inarijärvi
stays below optimum zone for Carex species
during spawning period, the direction of the change
between reference period and scenario period is positive.
Extent of frozen productive zone describes how
much of the littoral productive zone is frozen during
winter. It is important factor affecting organisms
(aquatic vegetation, invertebrates and eggs of autumn
spawning fish species) that can’t tolerate freezing.
Decrease of extent of frozen productive zone over 10
percentage points is positive effect. Also changes in
other similar indicators depending of water level changes
during ice covered period i.e. extent of ice pressure
zone and magnitude of winter drawdown have similar
positive effects.
Table 1. Water level fluctuation indicators calculated with Regcel model and assessment of effect of climate change on environment.
“+” = positive effect, “-“ = negative effect.
Water level fluctuation indicator
Reference period
1971–2000
Climate change
scenario
2040–2069
Effect
Magnitude of spring flood (m) 0.03 -0.01
Water level change during growing season (m) 0.21 0.10 +
Maximum vertical extension of the Carex zone (m) 0.24 0.25
Extent of frozen productive zone (%) 33.63 21.99 +
Extent of ice pressure zone (%) 50.83 37.18 +
Extent of disturbed productive zone (%) 38.27 32.07 +
Water level rise during the nesting of birds (m) 0.18 0.13 +
Magnitude of winter drawdown = water level decrease during the ice
cover period (m)
1.20 0.92 +
Decrease of water level during spawning of northern pike (m) 0.00 0.01
Minimum water depth in the Carex zone during the spawning of northern
pike (m)
-0.44 -0.23 +
Mean number of days per year when water level > 119,35 9.7 6.4 +
47

Magnitude of monthly water conditions
Timing of annual extremes
Magnitude and duration of annual extremes
Flow (m3/s)
Variation coefficient (%)
Flow (m3/s)
Variation coefficient (%)
Day number
Variation coefficient (%)
200
60 300
50
250
90
160
120
80
40
0
Janmeameameameameameameamean
0 mean mean mean mean
Feb-
Mar-
Apr-
May-
Jun-
Jul-
Aug-
Sep-
Oct-
Nov-
Dec-
1-Day-Min-
Date
Mean Regulated
CV Regulated
200
150
100
50
Mean Recalculated
50
40
30
20
10
CV Recalculated Mean Regulated
CV Regulated
0
250
200
150
100
50
0
1-day-minimum
flow
1-day-maximum
flow
3-day-minimum
flow
3-day-maximum
flow
7-day-minimum
flow
7-day-maximum
flow
30-day-minimum
flow
30-day-maximum
flow
90-day-minimum
flow
Mean Recalculated
CV Recalculated
Figure 2. Magnitude of monthly water conditions and magnitude and duration of annual extremes in the Pasvik River. Comparison
of current (1970–2000 deep blue, red line) and climate change (2040–2069, light blue, black line) hydrology.
80
70
60
50
40
30
20
10
90-day-maximum
flow
40
30
20
10
0
Day number
250
200
150
100
50
0
Timing of annual extremes
1-Day-Min-
Date
Mean Regulated
CV Regulated
Variation coefficient Timing of (%) annual Number/duration extremes of pulses (d)
90
35
Day number
Variation coefficient (%)
250
80
90
30
70
80
200
150
100
50
0
1-Day-Max-
Date
Mean Regulated
Mean Recalculated
CV Regulated
CV Recalculated
1-Day-Max-
0 Date
1-Day-Min-
Mean Recalculated Date
60
50
40
30
20
10
CV Recalculated Mean Regulated
CV Regulated
0
Figure 3. Timing of annual extremes and frequency and duration of high and low pulses in the Pasvik River. Comparison of current
(1970–2000, deep blue, red line) and climate change (2040–2069, light blue, black line) hydrology.
25
20
15
10
5
0
Frequency and duration of high and low pulses
Number of High- Number of Low-
1-Day-Max-
Pulses
Pulses
Date Mean Regulated
Mean Recalculated
CV Regulated
CV Recalculated
70
60
50
40
30
20
10
0
Duration- Hi-
Pulse
Variation coefficient (%)
Duration- Lo -
Pulse
Mean Recalculated
CV Recalculated
200
180
160
140
120
100
80
60
40
20
0
48

Productive littoral zone between mean high water
and mean low water is disturbed by wave action during
open water period and by freezing and ice pressure
during ice covered period. These disturbances also
have an effect on littoral biota restricting survival of
sensitive species. Change in extent of disturbed productive
zone, although only 6.2 percentage points, is
positive.
Water level rise during the nesting of birds can
destroy nests near the water. Decrease in water level
rise is only 0,05 m but the direction of change is
positive and it can have an positive effect on nesting
success of birds.
Wave action induced erosion is significant disturbance
affecting littoral habitats on Lake Inarijärvi and
water levels above 119.35 m.a.s.l. are considered to
be the conditions when erosion in Lake Inarijärvi is increasing
substantially (Puro-Tahvanainen et al. 2011).
Decrease in mean number of days per year when water
level is greater than 119.35 m.a.s.l. has a positive
effect on erosion sensitive shores mitigating disturbance
caused by erosion.
DHRAM
DHRAM analysis was applied to water flow data from
the Kaitakoski station, situated at the outlet of Lake
Inarijärvi. The difference between flow situations was
also quite clear; with very much higher spring flow and
lower flow during summer.
There are relatively large changes between the reference
period and the climate change scenario period
in flow situations according to the DHRAM analysis.
When comparing monthly water level fluctuations,
there is a clear change from summer months to winter
as a consequence of climate change. However, summer
and the increase in variation are the main factors
affecting the situation (Figure 2).
Magnitude and duration of annual extremes shows
no significant change in terms of final impact points
although the values of the extremes have changed
slightly (Table 1, Figure 2). The timing of annual extremes
has changed significantly, but there has been
also increasing variation as a consequence of climate
change (Figure 3). Change in flood timing reaches 2
final impact points, which is quite significant from the
point of view of ecology.
More relevant ecological change is visible in the
frequency and duration of high and low pulses (Figure
3). There are more high and low pulses, but mean
duration is significantly lower. This situation can cause
environmental stress for most biota, which cannot
adapt to rapid changes (Richter et al. 1996).
There are no big differences in the rate and frequency
of change in conditions. DHRAM analysis
showed only a weak trend; a total of impact 2 points
were reached, which according to Black et al. (2000)
indicates a low risk of impact.
Conclusions
Climate change impacts on hydrology and water level
fluctuation indicators were analysed in the Pasvik
River catchment. Two different hydrological analysis
systems were applied. Water level data for Lake Inarijärvi
was analysed using Regcel model. The model
enables assessment of the ecological impacts of water-level
regulation on aquatic macrophytes, benthic
invertebrates, fishes and nesting of waterfowl.
The results in water level fluctuation indicators
showed that changes in water level fluctuation would
have mainly positive effects on environment brought
by decrease in fluctuation. Water level change during
growing season in Lake Inarijärvi being smaller
in scenario period than in reference period promotes
zonation of littoral vegetation and would have a positive
effect on shore habitat diversity. Decrease of
extent of the zone disturbed by wave action, extent
frozen productive zone, extent of ice pressure zone
and magnitude of winter drawdown all have positive
effects on survival of sensitive littoral biota. Decrease
in water level rise can have a positive effect on nesting
success of birds and decrease in mean number of
days per year when water level is greater than 119.35
m.a.s.l. has an positive effect on erosion sensitive
shores.
General flow data for the Pasvik River was analysed
by the DHRAM programme. The method is rapid,
but the biological response was partly unclear.
The analysis showed that the climate change induced
change in flow regimes would have low risk of impact.
Both Regcel and DHRAM method can be effectively
used to assess hydrological and ecological effects of
climate change on lakes and rivers using measured
and simulated water level and discharge data.
49

References
Black, A.R., Bragg, O.M., Duck, R.W., Jones, A.M., Rowan, J.S., Werritty A. 2000: Anthropogenic Impacts upon the Hydrology
of Rivers and Lochs: Phase I A User Manual Introducing the Dundee Hydrological Regime Assessment Method. -
SNIFFER Report No SR(00)01/2F. 32 p.
IPCC. 2000: IPCC Special Report, Emission Scenarios. (Nakicenovic, N., Swart R. (eds.)). Cambridge University Press.
UK. 570 p.
Jylhä, K., Ruosteenoja, K., Venäläinen, A., Tuomenvirta, H., Ruokolainen, L., Saku, S., Seitola, T. 2009: Arvioita Suomen
muuttuvasta ilmastosta sopeutumistutkimuksia varten. ACCLIM-hankkeen raportti 2009. Ilmatieteen laitos, Reports
2009:4. Helsinki. 102 p. (in Finnish)
Puro-Tahvanainen, A., Aroviita, J., Järvinen, E. A., Kuoppala, M., Marttunen, M., Nurmi, T., Riihimäki, J., Salonen, E. 2011:
Inarijärven tilan kehittyminen vuosina 1960-2009. Suomen ympäristökeskus. Suomen ympäristö 2011, 19. Helsinki, 89 p
http://www.ymparisto.fi/default.asp?contentid=393911&lan=fi (in Finnish with abstracts in Swedish and in English)
Richter, B.D, Baumgartner, J.V., Powell, J. & Braun, D.P. 1996: A method for assessing hydrological alteration within a river
network. Conservation Biology 10: 1163–1174.
Vehviläinen, B., Huttunen, M., Huttunen, I. 2005: Hydrological forecasting and real time monitoring in Finland: The watershed
simulation and forecasting system (WSFS). In: Innovation, Advances and Implementation of Flood Forecasting
Technology, conference papers, Tromsø, Norway, 17–19 October 2005.
Veijalainen, N., Jakkila, J., Nurmi, T., Vehviläinen, B., Marttunen, M., Aaltonen, J. 2012: Suomen vesivarat ja ilmastonmuutos
– vaikutukset ja muutoksiin sopeutuminen, WaterAdapt-projektin loppuraportti (Finland´s water resources and climate
change – Effects and adaptation, final report of the WaterAdapt –project). Suomen ympäristökeskus, Suomen ympäristö
16/2012, Helsinki, 138 p. (in Finnish)
The Pasvik River.
Photo: O. Pershin.
50

Carex zone in the Pasvik River.
Photo: Juha Riihimäki.
The rocks of Lake Inari reflect water level fluctuation.
Photo: Heidi Salow.
51

3 Toxic substances on the sediments of
the Pasvik River
VLADIMIR DAUVALTER, GUTTORM N. CHRISTENSEN, HELÉN JOHANNE ANDERSEN
Lakes and reservoirs (and their sediments as storage
of physical and chemical disintegration products of a
wide range of chemical substances) serve as collectors
of all substances delivered into their catchment
area. Sediments are an important information source
about climatic, geochemical and environmental
conditions that existed in the catchment area and in
the reservoirs itself, which allows estimating today’s
ecological state of the air and aquatic environments.
Heavy metals’ concentrations in the sediments allow
estimating contamination intensity and history of the
investigated lakes.
Sediment cores from the river-and-lake system of
the Pasvik River were used to estimate the effect of
the Pechenganikel mining and metallurgical company
activity on the waterway’s status. Maximum concentration
of the investigated heavy metals (nickel (Ni),
copper (Cu), cobalt (Co), zinc (Zn), cadmium (Cd),
lead (Pb), mercury (Hg) and arsenic (As)) in the sediment
surface layers was identified in Lake Kuetsjarvi
receiving waste waters from the Pechenganikel
Company. Heavy metal content decrease in the sediment
surface layers is observed downstream the
Pasvik River from waste water inflow, although contamination
is considerably high. In the lakes polluted
only by air transport and household sewage there was
no increase in the Ni, Cu, Co, Zn content emitted by
the Pechenganikel but great increase of chalcophile
elements’ (Pb, Cd, Hg and As) concentration was discovered.
The average sedimentation rate in the lakes
under study appeared to be higher (1–3 mm/year)
than on an average for the lakes of the northern Fennoscandia
(less than 1 mm/year). Phosphorus content
increase is detected towards sediment surface in some
lakes which may give evidence of eutrophication
process development.
Materials and methods
Sediment cores for heavy metal analysis were collected
at five stations of Lake Kuetsjarvi (1. White
Stone, 2. Gulf Stream, 3. Salmijarvi, 4. Kolosjoki, 5.
Shuonijoki) and in lakes Ruskebukta, Vaggatem and
Skrukkebukta (Introduction, Figures 1 and 2). Sediment
cores for persistent organic pollutants (POPs)
were collected in lakes Vaggatem, Kuetsjarvi and
Skrukkebukta. The upper 3 cm of the sediment core
was sliced in 1 cm sections.
Industrial impact on the lake ecosystem was determined
using the contamination factor (C f
) of each
priority heavy metal contaminant (Ni, Cu, Co, Zn, Pb,
Cd, Hg and As) (method of Håkanson, 1980, 1984).
i
In this approach the following classification of C f
was
used: C fi

other lakes under study. The highest Pb concentrations
were detected in lakes Ruskebukta and Skrukkebukta.
Generally, the average background concentrations
of almost all heavy metals (except Cd) in the
Pasvik River reservoir catchment areas are higher
than the average background concentrations in sediments
of the North-West of Murmansk Region and
border area of the neighbouring countries (Kashulin
et al. 2009).
According to factor analysis the chemical composition
of the sediment background layers is influenced
by natural peculiarities of the Pasvik River drainage
(physical parameters and geochemical peculiarities)
and the processes in the lake itself (biological processes,
changes in reductive-oxidative conditions etc.).
Three groups were clearly identified by cluster analysis:
the first group includes channel reservoirs (lakes
Ruskebukta, Vaggatem and Skrukkebukta), the
second is the Lake Kuetsjarvi stations 1 and 3 with
minimum sedimentation rate where the deepest sediment
layers are the most approximated to the natural
background values and the third is the Lake Kuetsjarvi
station 4 and 5 with the maximum sedimentation rate
where background sediment layers most likely were
never reached.
Vertical distribution of elements in the sediments
Waste water from the Pechenganikel integrated plant
is the main contamination source of Lake Kuetsjarvi
and surface waters in the territory of industrial area.
heavy metals (Ni, Cu, Zn, and Fe (iron)) and organic
substances (xanthates) are the main components of
the waste waters (Dauvalter 2002).
Even though the Pechenganikel reduced its heavy
metal discharge into Lake Kuetsjarvi there is no observed
decrease of concentrations in the sediments
in Lake Kuetsjarvi and almost all the elements have
surface maximums. Only Hg is characterized by maximum
concentrations at depths 2–4 cm of sediment cores
almost at all of the lake stations (Figure 1). No significant
changes were found in the vertical distribution
of Ni, Cu, Co and Zn concentrations in sediments of
Ruskebukta and Vaggatem situated upstream from
Lake Kuetsjarvi. A slight increase of Ni and Cu concentrations
is observed in the upper 4 cm sediments
of Lake Vaggatem, which is connected with airborne
industrial pollution from the Pechenganikel. However
an increasing trend of concentrations of chalcophile
elements (Pb, Cd, Hg and As) was discovered in
the surface layers of these lakes in comparison with
the background content. The greatest increase was
observed in Lake Ruskebukta for Hg. This phenomenon
is probably connected to the global atmospheric
pollution of the Northern hemisphere and not directly
connected with the Pechenganikel because this part
of the Pasvik River catchment area is not significantly
affected by the discharges (Pacyna & Pacyna 2001;
Hagen et al. 1991). A slight decrease of Cd concentration
is also found in the upper layer in Lake Vaggatem.
In the sediments of Lake Skrukkebukta situated
downstream from Lake Kuetsjarvi the maximum concentrations
of Ni, Cu, Co, Cd and Pb were found in
the upper 1 cm layer (Figure 1). A significant increase
of heavy metal concentrations as compared to the
background was detected in the upper 3 cm of sediments.
Concentration decrease of aluminium (Al), magnesium
(Mg), potassium (K) and calsium (Ca) is observed
towards to sediment surface of Lake Kuetsjarvi
(Figure 2) which can be related to the inflow of a large
amount of sulphate in the content of waste waters
from the Pechenganikel Company causing release of
alkaline and alkaline-earth metals and Al from the suspended
particles and sediments and their transition
into a dissolvable form (Baklanov & Makarova 1992).
The sulphate content in the water of Lake Kuetsjarvi
is 2–3 times higher than in all the other lakes of the
Pasvik River system under study. Such regularity was
also discovered in sediments of Lake Skrukkebukta.
Increase of Mn and Fe concentrations is observed
towards the sediment surface almost at all the stations
of Lake Kuetsjarvi. Increase of phosphorus (P)
content is also observed in the surface layers of sediments
of Lake Kuetsjarvi and the other lakes. This can
indicate development of eutrophication processes, related
to the inflow of household sewage and the river
flow regulation and resulting into the flow deceleration,
stagnations, and, finally, accumulation of nutrients
in aquatic ecosystems. Phosphorus accumulated in
sediments can be a source of this nutrient permeating
into the water layers (Lennox 1984; Sandman et al.
1990; Shaw & Prepas, 1990).
Factor analysis was performed to discover the main
factors influencing chemical composition formation of
the sediments. The first factor is effect of discharged
waters and emissions from the Pechenganikel Company,
the second factor is natural processes taking
place in the water layer and the sediments of reservoirs
and the third factor comprises reductive-oxidative
processes and eutrophication.
53

Cu
0
0 500 1000 1500 2000 2500 Ni 0 1000 2000 3000 4000 5000 Cu 0 40 80 120 160 Ni 0 100 200
0
0
0
5
10
Sediment depth, cm
15
Skrukkebukta
Vaggatem
Ruskebukta
5
10
Sediment depth, cm
5
10
15
1
2
3
4
5
5
10
15
Skrukkebukta
Vaggatem
Ruskebukta
Zn 0 200 400 600 800 Co 0 100 200 300 400 500 Zn 0 40 80 120 Co 0 20 40
0
0
0
0
5
10
15
Skrukkebukta
Sediment depth, cm
Vaggatem
Ruskebukta
5
10
Sediment depth, cm
15
Skrukkebukta
Vaggatem
Ruskebukta
0 10 20 30 40 50 Cd 0 0.1 0.2
Pb 0 10 20
0
0
5
10
Sediment depth, cm
15
Skrukkebukta
Vaggatem
Ruskebukta
5
10
Sediment depth, cm
15
Skrukkebukta
Vaggatem
Ruskebukta
0 0.4 0.8 1.2
As 0 4 8
Hg 0 0.1 0.2
0
0
5
10
Sediment depth, cm
15
Skrukkebukta
Vaggatem
Ruskebukta
5
10
Sediment depth, cm
Sediment depth, cm
15
1
Sediment depth, cm
2
3
4
5
5
10
15
1
2
Sediment depth, cm
5
3
4
5
10
15
1
2
Sediment depth, cm
5
3
4
Cd 0 1 2 3 4
0
Pb
0
5
10
15
3
Sediment depth, cm
1
2
4
5
5
10
15
3
Sediment depth, cm
1
2
4
5
As 0 40 80 120
0
Hg
0
5
10
15
1
Sediment depth, cm
4
5
2
3
5
10
15
1
Sediment depth, cm
4
5
2
3
15
Skrukkebukta
Vaggatem
Ruskebukta
Figure 1. Vertical distribution of heavy metal concentrations (µg/g) in bottom sediment columns of Lake Kuetsjarvi (stations 1–5) and in the Pasvik River lakes (red = Skrukkebukta, green =
Vaggatem, blue = Ruskebukta).
54

Al
0
0 10000 20000 30000
Mg
0
0 20000 40000 60000
Sediment depth, cm
5
10
15
1
2
3
4
5
Sediment depth, cm
5
10
15
1
2
3
4
5
K
0
0 2000 4000 6000
Na
0
0 400 800 1200
Sediment depth, cm
5
10
1
2
3
15
4
15
4
5
5
Figure 2. Vertical distribution (sediment depth, cm) of Al, Mg, K and Na concentrations (µg/g) in sediments of
Lake Kuetsjarvi.
Sediment depth, cm
5
10
1
2
3
Element distribution in the surface layers of bottom
sediments
Pechenganikel emissions are the main source of increased
heavy metal concentrations of the Pasvik River
system. This is especially intensive in Lake Kuetsjarvi.
The highest Ni and Cu concentrations exceeding
the background values 10–380 times are observed at
a distance ≤10 km from the Pechenganikel (Dauvalter
1994, 1995, 1999). The background concentration
exceeding reduces up to 3–7 times at a distance of 10
to 40 km from the pollution source. Co concentrations
were 4–10 times higher than the background at a distance
of ≤15 km from the pollution source and up to 3
times higher in other lakes, which is evidence of the
emissions’ impact.
The main part of the industrial waste water from the
Pechenganikel enters Lake Kuetsjarvi. The highest
concentrations of heavy metals were observed at the
deepest station 1 (Ni, As), at stations 4 and 5 (Cu, Cd,
Pb, Hg) situated nearest to the Pechenganikel waste
water discharge area and also at station 3 (Zn, Co),
closest to the channel connecting Lake Kuetsjarvi and
the Pasvik River watercourse. In the lower course of
the Pasvik River in Lake Skrukkebukta the maximum
concentrations of Ni, Cu, Co, Zn, Cd and Pb were
found in the surface sediment layer which is related
to the contaminated water inflow from Lake Kuetsjarvi
Concentration increase of chalcophile elements
(Pb, Cd, As and Hg) was observed in the upper sediment
layers in the lakes of the Pasvik River upper
stream. High concentrations were observed in Lake
Ruskebukta (the highest As and Hg concentrations
except for Lake Kuetsjarvi), which proves that atmospheric
emissions of the Pechenganikel are not
the major contamination source of chalcophile elements.
The highest Fe and Mn concentrations in the
surface layers of sediment were recorded at station 1
of Lake Kuetsjarvi and the highest concentrations of
alkaline-earth metals (Ca, Mg and Sr (strontium)) and
P are also discovered in Lake Kuetsjarvi. Increased P
content was observed in Ruskebukta, which is related
to the water flow regulation and household waste
water discharge from the populated localities on lake
shores.
Factor analysis was performed to identify the main
factors influencing the formation of chemical composition
of today’s sediments in the surface 1 cm layer
of the investigated reservoirs. The first factor consists
of the development of lakes’ contamination processes
and the second factor combines values characterizing
the biological activity of the reservoirs as well as some
of the main elements in the Earth crust (Na (sodium)
and Mg). Most likely the processes taking place in the
55

eservoirs play the main role despite of the heavy contamination
in Lake Kuetsjarvi.
Two groups of reservoirs were determined by cluster
analysis. The first group is the water area of Lake
Kuetsjarvi (stations 2–5). The second group forms of
the lakes situated downstream and upstream from Lake
Kuetsjarvi (Skrukkebukta, Ruskebukta and Vaggatem),
which are characterized by less contamination
of the sediment surface layers due to greater distance
from the smelters. Station 1 of Lake Kuetsjarvi stands
apart, probably because it is the deepest station and
specific physical chemical and geochemical peculiarities
of this water area made a contribution to the
sediment chemical composition. This station is also
characterized by the highest contamination degree
among all the investigated stations.
Factor and degree of contamination
Contamination intensity of reservoirs can be estimated
by comparing heavy metal concentrations in the
surface layer to the background values of sediments.
Determination methods of factor and degree of contamination
of water ecosystems by heavy metals in
sediments using C f
and C d
are described in Håkanson
(1980, 1984). Determination methods of sediment
contamination by industrial enrichment factor are
described in Alhonen (1986), Ouellet & Jones (1983)
and Tolonen & Jaakkola (1983).
Maximum values of C f
(high contamination) for almost
all of the studied heavy metals are observed in
the sediments of Lake Kuetsjarvi. Only for Zn (except
for station 3) and Pb the moderate and considerable
contamination were observed. Stations 1 (Ni), 3 (Zn,
Co and As), 4 (Cu), 5 (Cd, Pb and Hg) are characterized
by the higher contamination factors. In general
the highest value of contamination degree was recorded
at the station 1.
Impact of the waste water from the Pechenganikel
as significant Ni contamination was also observed
downstream in Lake Skrukkebukta. The C f
values for
Cu and Cd are on the borderline between moderate
and considerable and moderate contamination is observed
for the other metals. Among the lakes situated
upstream from the Pechenganikel Company the high
Hg contamination is found in Lake Ruskebukta. Low
and moderate values of C f
are observed for other metals
in lakes Ruskebukta and Vaggatem.
In general, Lake Kuetsjarvi (high value of C d
) is
characterized by the maximum contamination by all
the studied contaminants. Lake Skrukkebukta has C d
value on the boundary between low and moderate
and low values of C d
are recorded in lakes Vaggatem
and Ruskebukta.
Persistent organic pollutants in sediments
Previous screening studies of persistent organic pollutants
(POPs) in bottom sediments revealed higher
levels of several environmental contaminants in Lake
Kuetsjarvi compared to other lakes in the Pasvik River
(Christensen et al. 2007).
The highest concentrations of ∑PCB were measured
in sediments from Lake Kuetsjarvi downstream
from the Nikel city (Figure 1). The levels of ∑PCB in
lake sediments from Vaggatem and Skrukkebukta were
comparable. The levels in surface sediment (0–1
cm) were approximately 4 times higher in Lake Kuetsjarvi
compared to Vaggatem and Skrukkebukta. The
highest concentrations of PCB were detected in the
2–3 cm layer from Lake Kuetsjarvi. These results are
comparable with previous study from the Pasvik River
(Christensen et al. 2007) and the concentrations of
∑PCB in Lake Kuetsjarvi are elevated compared to
other lakes in Northern Norway (Skotvold et al. 1997,
Christensen et al. 2008).
The highest concentrations of ∑DDT were measured
in sediments from Skrukkebukta (Figure 2). DDT
was also detected in the surface sample from Vaggatem.
The concentrations of DDT in all the sediment
samples from Lake Kuetsjarvi were below the detection
limit. There are no known sources of DDT in the
area. However, DDT was previously used as pesticide
in agriculture but tit was banned in the early 1970s. In
a previous study the highest levels of DDT were detected
in sediments from Lake Kuetsjarvi (Christensen
et al. 2007).
Polybrominated diphenyl ethers (PBDE) were only
detected in sediments from Skrukkebukta and Vaggatem
(Christensen et al. 2015).
Based on the results from this study there is no
clear time trend of the historical development of POPs
in the sediments from these lakes. It is recommended
that a more extensive sediment survey be carried out
with dating of the sediments, which will give detailed
information about historical trends for POPs and heavy
metals.
The higher levels of PCB and mercury in the analysed
sediment samples downstream from Nikel compared
to upstream clearly indicate emissions of these
compounds from Nikel. The source might be related to
the activity in the metallurgical smelter or other industrial
activity, contaminated areas in the city or landfills.
56

The results from this sediment study and the results
from the study of contaminants in fish are comparable
in that sense that the highest levels of POPs
are found downstream from Nikel. However there are
several very interesting questions that need further
investigations: the time-trends for PCB, DDT, PBDE
and mercury and where the sources in the Nikel city
are, for example.
PCB
20
18
16
14
ng/g dw
12
10
8
6
0-1 cm
1-2 cm
2-3 cm
4
2
0
Vaggatem Kuetsjärvi Skrukkebukta
Figure 1. Levels of ∑PCB (ng/g dw) in three layer (0–1 cm blue bars, 1–2 cm red bars, 2–3 cm green bars) of bottom
sediments from lakes Vaggatem, Kuetsjarvi and Skrukkebukta.
4
3,5
3
DDT
ng/g dw
2,5
2
1,5
0-1 cm
1-2 cm
2-3 cm
1
0,5
0
Vaggatem Kuetsjärvi Skrukkebukta
Figure 2. Levels of ∑DDT (ng/g dw) in three layer (0–1 cm blue bars, 1–2 cm red bars, 2–3 cm green bars) of bottom
sediments from lakes Vaggatem, Kuetsjarvi and Skrukkebukta.
57

Conclusions
The highest background concentrations of most of
the heavy metals (Ni, Zn, Co, Cd, Hg and As) in the
sediments are observed in the southern part of Lake
Kuetsjarvi, which is accounted for by the geochemical
and morphometric peculiarities of the lake and its
catchment area. In general the average background
concentrations of almost all the heavy metals (except
Cd) in the catchment reservoirs of the Pasvik River
are higher than the average background concentrations
in the North-West of Murmansk Region and the
border territories.
The Pechenganikel emissions result into the maximum
concentrations of all the investigated heavy
metals in the surface layers of the sediments of Lake
Kuetsjarvi. Almost all heavy metals have surface
maximum and despite the reduction of discharge into
Lake Kuetsjarvi and atmospheric emissions by the
Pechenganikel there is no observable decrease in the
content. Downstream from the Pechenganikel in Lake
Skrukkebukta maximum concentrations of Ni, Cu,
Co, Cd, and Pb are found in the upper 1 cm layer of
sediments and large increase of heavy metal concentrations
in comparison to the background is distinguished
in the upper 3 cm of the sediments.
In lakes Vaggatem and Ruskebukta upstream from
the Pechenganikel no great changes in the vertical
distribution of Ni, Cu, Co and Zn concentrations are
found in the sediments, although there is a slight increase
of Ni and Cu concentrations in the upper 4 cm
in Lake Vaggatem. However, a slight increase in concentration
of chalcophile elements was discovered in
the surface layers of lakes Ruskebukta and Vaggatem
in comparison to the background content. The largest
increase is observed in Lake Ruskebukta for Hg.
In general Lake Kuetsjarvi (high value of C d
contamination
degree) is characterized by maximum contamination
by all the studied contaminating elements.
Lake Skrukkebukta has the C d
value on the boundary
between low and moderate. Low values of C d
are
found in lakes Vaggatem and Ruskebukta, which are
the least polluted of the studied lakes.
Industry is the main source of contaminants in the Pasvik region. Photo: Sergey Sandimirov
58

References
Alhonen P. 1986: Heavy metal load of Lake Iidesjarvi as reflected in its sediments. Aqua Fennica 16(1): 11–16.
Baklanov А.А, Makarova Т.D. 1992: Contamination with sulfuric gas in the area of Soviet-Norwegian border. Ecological and
geographical problems of the Kola North: 114–129. Kola Science Center RAS. Apatity. (in Russian)
Christensen, G.N., Savinov, V. Savinova, T. 2007: Screening studies of POPs levels in bottom sediments from selected
lakes in Paz watercourse. Akvaplan-niva report 3665.01.
Christensen, G., A. Evenset, S. Rognerud, B.L. Skjelkvåle, R. Palerud, E. Fjeld, Røyset, O. 2008: National lake survey
2004–2006, PART III: AMAP. Status of metals and environmental pollutants in lakes and fish from the Norwegian part of
the AMAP region. Akvaplan-niva rapport 3613.01. SFT TA 2363-2008.
Christensen, G.N., Andersen, H.J., Dahl-Hansen, G. 2015: Contaminants in fish and sediments from the Pasvik River.
Akvaplan-niva report. 5943.01.
Dauvalter V. 1994: Heavy metals in lake sediments of the Kola Peninsula, Russia. Science of the Total Environment 158:
51–61.
Dauvalter V.А. 1995: Heavy metal concentrations in sediments of the Kola Peninsula lakes as an indicator of contamination
of aquatic ecosystems. Problems of chemical and biological monitoring of ecological state of water bodies of the Kola
North: 24–35 Kola Sience Center RAS. (in Russian)
Dauvalter V.А. 1999: Appropriateness of sedimentation in water objects of European Subarctic (nature protection aspects
of problem). DrSci Thesis. Kola Science Center RAS. Apatity. 399 p. (in Russian)
Dauvalter V.А. 2002: Chemical composition of subarctic lake sediments under influence of mining and metallurgical activities
- Izvestiya of Russian Academy of Sciences. Geography series 4: 65–73. (in Russian)
Hagen L.O., Aarnes M.J., Henriksen J.F., Sivertsen B. 1991: Basisundersokelse av luftforurensinger i Sor-Varanger
1988–1991. Oslo: NILU-report 67/91. 89 p. (in Norwegian)
Håkanson L. 1980: An ecological risk index for aquatic pollution control – a sedimentological approach. Water Research 14:
975–1001.
Håkanson L. 1984: Sediment sampling in different aquatic environments: Statistical aspects. Water Resources Research
20(1): 41–46.
Kashulin N.A., Sandimirov S.S., Dauvalter V.А., Terentiev P.M., Denisov D.B. 2009: Ecological catalogue of lakes of Murmansk
Region. The North-western part of Murmansk Region and boundary territory of the contiguous countries. Kola
Sience Center RAS. Apatity. 226 p. (in Russian)
Lennox L.J. 1984: Sediment-water exchange in Lough Ennel with particular reference to phosphorus. Water Resources
18(12): 1483–1485.
Norton S.A., Dillon P.J., Evans R.D., Mierle G., Kahl J.S. 1990: The history of atmospheric deposition of Cd, Hg and Pb
in North America: Evidence from lake and peat bog sediments. in Lindberg S. E. et al. (Eds.). Sources, Deposition and
Capony Interactions. V. III, Acidic Precipitation. New York: Springer-Verlag. 73–101.
Ouellet M., Jones H.G. 1983: Paleolimnological evidence for the long-range atmospheric transport of acidic pollution and
heavy metals into Quebec, Canada. Canadian Journal of Earth Sciences 20: 23–26.
Pacyna J.M., Pacyna E.G. 2001: An assessment of global and regional emissions of trace elements to the atmosphere
from anthropogenic sources worldwide. Environmental Reviews 4: 269–298.
Sandman O, Eskonen K, Liehu A. 1990: The eutrophication history of Lake Särkinen, Finland and the effects of lake aeration.
Hydrobiologia 214: 191–199.
Shaw J.F.H., Prepas E.E. 1990: Relationships between phosphorus in shallow sediments and in the trophogenic zone of
seven Alberta lakes. Water Research 24(5): 551–556.
Skotvold, T., Wartena, E.M.M., Rognerud, S. 1997: Heavy metals and persistent organic pollutants in sediments and fish
from lakes in Northern and Arctic regions of Norway. Statlig program for forurensningsovervåkning, SFT rapport 688/97.
98 p.
Tenhola M., Lummaa M. 1979: Regional distribution of zinc in lake sediments from eastern Finland. Symposium on Economic
Geology, Dublin, Ireland, 26–29 August: 67–73.
Tolonen K., Jaakkola T. 1983: History of lake acidification and air pollution studied on sediments in South Finland. Annales
Botanici Fennici 20: 57–78.
59

4 Plankton communities of the Pasvik
River
DMITRII DENISOV
Phytoplankton
Phytoplankton communities are an integral part of the
status assesment of water bodies in subarctic regions.
Phytoplankton algae play an important role in primary
production of aquatic ecosystems. Various indicators
of the status of algae communities are successfully
used in assessment of water trophic status, level of
organic pollution and intensity of eutrophication processes.
The structure and taxonomic composition of
phytoplankton communities in lake-and-river systems
in different seasons are necessary for building and
improvement of bioindication systems and expansion
of understanding of the diversity of conditions within
one water body depending on environment and industrial
load. Data on the composition of communities
is important also for identifying the crucial factors of
water bodies’ development in high-latitude regions in
the course of local and global changes in the natural
environment. Special interest is held by the study of
annual phytoplankton dynamics in monitoring longterm
industrial pollution and changes in the climate
system dynamics.
Materials and methods
Different areas of the Pasvik watercourse traditionally
used for assessment of the water quality and the
aquatic ecosystem status were sampled. The sampled
water bodies were Rajakoski, Vaggatem, Ruskebukta,
Tjerebukta, Skrukkebukta and Lake Kuetsjarvi
(Introduction, Figure 1).
Sampling for assessment of species composition,
abundance and biomass of phytoplankton was performed
in 2012 in July and August; additional samples
from Lake Kuetsjarvi were taken in June for assessment
of the seasonal dynamics of phytoplankton indicators.
Sampling and analysis of phytoplankton samples
was performed according to the standards GOST
17.1.3.07-82 (RF) with the use of the recommended
standard methods according to the scheme adopted
at INEP KSC RAS. Species composition was identified
according to several field guides (Krammer 2000,
2002, 2003, Lange-Bertalot 2001, Tikkanen 1986, Barinova
& Medvedeva 1996, Krammer & Lange-Bertalot
1986, 1988, 1991а, 1991b). The taxonomic diversity
was assessed with Shannon-Weaver index (1949).
Phytoplankton pigments were determined to assess
the algae photosynthetic activity. The chlorophyll
a concentration and phytoplankton biomass in a water
body reflect its trophic status accurately enough according
to Kitaev’s trophic scale (Table 1).
Table 1. Kitaev’s trophic scale (1984)
Oligotrophic Мesotrophic Eutrophic Hypereutrophic
α β α β α β
Chlorophyll а, mg/m 3 48
Phytoplankton biomass, g/m 3 16
Table 2. Water quality classification according to saprobity index S (GOST 17.1.3.07-82)
Saprobity index Water quality class Pollution range
4.00 VI Highly polluted
60

Based on the taxonomic composition of phytoplankton,
assessment of water quality class was made
on the basis of saprobity index (S) using Pantle-
Buck method modified by Sladecek. (Pantle & Buck
1955, Sladecek 1967). Saprobity is the pollution of
the water body with organic substances. Water quality
classification according to GOST 17.1.3.07-82 (RF) is
presented in Table 2.
Results and discussion
Species composition and structure of phytoplankton
communities
Totally 95 algae taxa one order lower than genus
from seven systematic groups were identified in the
phytoplankton composition of the Pasvik river-andlake
system: Cyanophyceae – 9, Chlorophyta – 25,
Charophyceae – 8, Chrysophyceae – 5, Dinophyta –
8, Bacillariophyceae – 37, Euglenophyceae – 1. The
number of taxa at each station is presented in Table 3.
The species abundance (number of discovered taxa)
was the highest close to Lake Kuetsjarvi (Salmijarvi,
52 taxa) as a result of combination of different type
water masses and a current. Species abundances in
the sampling place were variable, second highest was
31 taxa in Rajakoski and third highest 27 taxa in Ruskebukta.
Lowest abundances were in Tjerebukta (11),
Vaggatem (18) and Skrukkebukta (19).
In different parts of the Pasvik River the structure
of communities, species composition and quantitative
parameters of phytoplankton show considerable
differences (Figures 1 and 2). Diatoms, blue-green
algae and yellow-green algae were found the most
abundant; green algae accounted for a large portion
(up to 51 %) in Lake Kuetsjarvi. Peridiniales (5 %) and
charophytes (< 1%) were rare at all the stations.
In Ruskebukta the phytoplankton abundance is two
orders higher than in other sections. The cause was a
mass occurrence of diatom Urosolenia eriensis, which
is a typical benthic species, and its transition to planktonic
state confirms eutrophication. This correlates
with the hydrochemical analysis results as Ruskebukta
has the highest concentrations of nutrients (primarily
nitrates and phosphates) in the Pasvik River.
Blue-green algae were the most abundant (up to 52
%) in Rajakoski, mostly due to abundance of Oscillatoria
tenuis, which is another sign of eutrophication.
Abundance of green algae separates Lake Kuetsjarvi
from the other stations. The phytoplankton communities
are considerably different in the southern and
northern parts of the lake. In the northern part yellowgreen
algae, diatoms and green algae are abundant;
in the southern part, where the polluted wastewater
from the Pechenganikel plant is discharged, bluegreen
algae were actively developing (Figure 1b). At
the outflow, where Lake Kuetsjarvi is connected to the
Figure 1. Phytoplankton communities of the Pasvik River in August-September 2012: а) all stations; b) Lake Kuetsjarvi.
61

Figure 2. Total phytoplankton abundance of the Pasvik River in August–September 2012, mln.cells/m 3 : а) differences
between Ruskebukta and other stations; b) differences between other stations.
Figure 3.Trophic status of the different parts of the Pasvik River according to Kitaev (1984): a) phytoplankton
photosynthetic pigments, mg/m 3 ; b) phytoplankton biomass, g/m 3 .
Pasvik River, the algae abundance was found to be
minimal while the range of species was large. This is
possibly a result of mixing of hydro-chemically different
water masses, which creates suitable conditions
for development of various phytoplankton species.
The highest indicators Shannon-Weaver index was
found at the stations Golfstream and Salmijarvi in
Lake Kuetsjarvi, which might be due to mixing of different
water masses. The permanent presence of nutrients
along with increased mineralization (amount of
all ions determined during water analysis (mg/l)) supports
development of non-typical subarctic plankton
communities with a large portion of green algae. The
lowest species diversity was found in Ruskebukta associated
with the absolute domination of Urosolenia
eriensis.
The amount of photosynthetic pigments in phytoplankton
is used in monitoring of the status, natural
seasonal processes, anthropogenic impact and pollution
level of water bodies in industrially developed regions
of the northern Kola Peninsula (Sharov 2004). It
is used as an indicator for assessment of phytoplankton
productivity and biomass (Vinberg 1960).
According to chlorophyll a concentration the trophic
status of the Pasvik River areas under study ranges
from α-oligotrophic (Skrukkebukta) to β-mesotrophic
(Kuetsjarvi, Kolosjoki) (Figure 3a). The highest concentrations
of chlorophyll a in 2012 were in the eutrophied
areas of Ruskebukta and Lake Kuetsjarvi; these
data correlate well with the indicators of phytoplankton
abundance and hydrochemical analysis results.
The concentrations of phaeopigments and carotenoids
were the highest in Lake Kuetsjarvi and Raja-
62

koski, which is a sign of a higher detritus concentration
in the water column or presence of aging plankton
populations with a slowed-down photosynthetic activity.
Ruskebukta had the lowest concentration of carotenoids,
which confirms active development of phytoplankton
and its high photosynthetic activity.
In 2012 the phytoplankton biomass in the Pasvik
river-and-lake system varied from 0.23 (Skrukkebukta)
to 2.94 g/m 3 (Lake Kuetsjarvi, Kolosjoki). The highest
biomass levels were typical of Lake Kuetsjarvi and
Ruskebukta and their trophic status may be regarded
as α- and β-mesotrophic; the other water bodies have
retained their oligotrophic status (Figure 3b). The average
biomass level of phytoplankton in the study
lakes did not exceed the biomass level of the Kola
Peninsula lakes: 0.6–2.5 g/m 3 for the tundra and forest
tundra lakes and 0.56–2.96 g/m 3 for the north taiga
lakes (Letanskaya 1974, Kupetzkaya et al.1976).
Seasonal dynamics of phytoplankton
The seasonal dynamics of phytoplankton communities
were studied in Lake Kuetsjarvi (station Golfstream).
The phytoplankton abundance is comparatively high
already in June with green algae being the most abundant,
the portion of diatoms is also large and dinophytes
and yellow-green algae are developing. In July
the total algae abundance remains at the same level
as in June but blue-green algae develop. Also the proportion
of green algae increases, diatoms decrease,
dinophytes virtually disappear and the abundance of
yellow-green algae also decreases considerably. Later
by August the total phytoplankton abundance decreases,
mostly because the growth of green algae slowed
down; yellow-green algae disappear entirely, the
portion of blue-green algae decreas and the abundance
of diatoms remained at the same level.
The seasonal dynamics of phytoplankton in Lake
Kuetsjarvi are not typical for subarctic lakes due to
intensive development of green algae, which start
active growth already in June. At the same time, the
abundance of diatoms and yellow-green algae, the typical
inhabitants of subarctic, is relatively low.
Saprobity index and water quality
Saprobity index was estimated for assessment of organic
pollution level for each research area. At the time
of study the saprobity index of the water bodies
varied within 1.2–1.89, which correlates with a relatively
low pollution level (GOST 17.1.3.07-82). The
lowest saprobity index was in Tjerebukta and the
highest in Ruskebukta, where there was massive development
of Urosolenia eriensis. Saprobity indices
were relatively high in Lake Kuetsjarvi: at the stations
Belyi kamen, Salmijarvi, and Kolosjoki the water quality
class was III whereas the stations Golfstream and
Shuonijoki are in the class II. This may be interpreted
as another proof of different conditions within this water
body.
Long-term dynamics of phytoplankton
Assessment of many-year dynamics of quantitative
indicators of phytoplankton, especially biomass, is
important for understanding the long-term changes
in the ecosystem of the Pasvik watercourse. A grow-
Figure 4. Long-term phytoplankton biomass dynamics (g/m 3 ): a) Lake Kuetsjarvi; b) Other water bodies of the
Pasvik River system.
63

ing trend in phytoplankton biomass was observed in
Lake Kuetsjarvi from 1994 to 2012 with all the values
indicating mesotrophic state. In the other water
bodies the maximum values were observed in 1998
and 2008 but in 2012 there was a decrease. No clear
trend has been determined, which is due to the different
conditions in each lake as well as to irregular
sampling periods in different seasons. The average
biomass values for Lake Kuetsjarvi were higher than
those for the other water bodies (Figure 4). The average
biomass values for the Pasvik watercourse system
seem to confirm that its trophic status is changing
from β-oligotrophic to β-mesotrophic.
Considerable changes have taken place in the
phytoplankton species composition. In August 1994
diatoms dominated in different areas of the Pasvik
River and the portion of blue-green algae was insignificant
(Sharov 2004). In July 1996 several groups
including green and yellow-green algae and diatoms
dominated. In September 2008 the same taxa dominated
in many areas but blue-green algae abundance
was increased compared to the previous years, which
may be associated with favorable meteorological factors,
water temperatures etc. The high abundance of
blue-green algae also implies a possibility of massive
phytoplankton development outbreaks that may take
place in favorable conditions.
In Lake Kuetsjarvi the changes in phytoplankton
species composition are more prominent still. Diatoms
and yellow-green algae that dominated earlier are
being gradually replaced by green and blue-green algae,
especially in the latest years. This may be regarded
as an indicator of a warming trend in the climate
change. Algae growth is also facilitated by reduction
of toxic load in the latest decade.
Periphyton
Study of periphyton in the Pasvik River has not been
conducted. However, in the rocky substrate in shallow
waters near the Janiskoski reservoir outflow an unusual
massive fouling formed of diatom Didymosphenia
geminata (Lyngb.) Schmidt colonies was discovered.
This phenomenon is known as “Brown plague: Didymo”
which has been a worldwide serious problem for
streaming water bodies with coldish conditions in the
latest years (International…, 2013).
The structure of colonies of D. geminata associated
with other algae, mainly with diatoms, creates firm slimy
algal mats on the rocky substrate covering the river
bed. Massive development of D. geminata does
not require a large amount of nutrients or higher water
temperature. The species is widespread but it is only
in the latest decades that mass development outbreaks
have been observed particularly in the areas
where they were not common before (International…
2013). The dense colonies disrupt the natural habitats
of typical arctic water fauna, including benthos
and fish, and the changes affect potential spawning
sites and trophic chains. Massive development of D.
geminata definitely poses a certain threat to the Pasvik
River ecosystem functioning and the occurrence
could be regarded as a sign of global climate change
(Lavery et al. 2014).
Zooplankton
Zooplankton is an integral component of aquatic
ecosystems. In subarctic lakes the main flows of organic
substances and energy from producers to higher
trophic levels go through the communities of Protozoa,
Rotatoria and Crustacea. Zooplankton plays an
important role in the determination of the resource potential
of the lakes as it holds the intermediate position
between bacterioplankton, phytoplankton, benthos
and fish. Prevalence of species needing higher water
quality is characteristic of northern water bodies,
which means higher sensitivity to industrial impact.
The taxonomic structure of zooplankton community is
a good indicator of the pollution degree of the water
body.
Zooplankton plays a significant role in the determination
of fishery productivity of the water body as it is
one of the feed resources for fish. In terms of feed the
most valuable organisms should be considered the
crustacean genuses Daphnia, Bosmina, Bythotrepes,
Eudiaptomus, Heterocope and Cyclops.
Materials and methods
Zooplankton was sampled at the same stations as
phytoplankton (Introduction, Figure 1). Quantitative
samples were taken with a bathometer (of 2 l and 6 l)
from the surface to the bottom every other meter with
distinguishing layers: surface–2 m; 2–5 m, 5–10 m,
10 m–bottom. All qualitative samples were taken with
a qualitative Apstein net. Lugol’s solution was used as
a fixative.
Sampling and the required calculations were performed
according to standard practices of hydrobiological
monitoring (Abakumov 1992). The calculation
64

Table 3. Structural and functional indicators of zooplankton.
Parameter Rajakoski Tjerebukta Ruskebukta Vaggatem Skrukkebukta
Abundance (N), 10 3 ind./m 3 76.4 37.6 239.8 67.7 75.2
Biomass (B), g wet weight/m 3 0.2 0.7 1.8 0.5 0.4
N Rot
: N Clad
: N Cop
, % 93.1:2.7:4.2 63.8:28.3:8.0 85.3:10.7:4.0 85.2:12.1:2.7 92.5:3.3:4.2
B Rot
: B Clad
: B Cop
, % 18.1:58.2:21.4 22.0:64.8:8.4 14.7:62.6:22.4 24.9:70.0:0.7 27.3:16.6:53.1
B Crust
/B Rot
4.5 3.7 5.8 3 2.6
N Clad
/N Cop
0.6 3.5 2.6 4.5 0.7
B 3
/B 2
0.2 0.2 0.3 0.1 1.7
Shannon index, bit/ind. 2.1 2.8 2.1 2.3 2.1
w=B/N, mg 0.002 0.02 0.007 0.007 0.005
Saprobity (water quality class) 1.8 (III) 1.7 (III) 2.1 (III) 1.7 (III) 1.9 (III)
Trophic state (Kitaev, 1984) α-oligotrophic β-oligotrophic α-mesotrophic α-oligotrophic α-oligotrophic
of individual mass of organisms was based on the ratio
of the length and body mass of planktonic Rotatoria
and Crustacea (Ruttner-Kolisko 1977; Balushkina
& Vinberg 1979). The calculations of the abundance
and biomass were performed with a statistical programme
package (Syarki 1996).
Saprobity index (Pantle & Bukk in Sladecek modification)
was calculated proceeding from the individual
characteristics of the species saprobity according to
standard practices. Water quality was evaluated by
hydrobiological indicators: the total number of organisms
(individuals/m 3 ), the total number of species, the
total of biomass (g/m 3 ), the number of the main groups
(individuals/m 3 ), the biomass of the main groups (g/
m 3 ), the number of species in the group, the main species
and the species indicative of saprobity (description,
% from the total number, saprobity).
According to the classification of lakes on the Kitaev’s
trophic scale (1984), the lakes with the biomass
of zooplankton 16 g/m 3 to a very high class (hypertrophic).
Results
The species detected in the zooplankton community
of the Pasvik watercourse were typical for oligotrophic,
cold lakes and rivers. The majority of the zooplankton
community in the sampling period was represented by
rotifers. Cladoceran “fine” filtrators Bosmina sp. and
Daphnia sp. were detected. Also calanoids Eudiaptomus
gracilis Sars and Eudiaptomus graciloides Lilljeborg,
which are sensitive to pollution and valuable
in terms of fish feed, were found in all studied lakes
except in Skrukkebukta. The amount of zooplankton
species varied from 12 to 14; at Skrukkebukta 8 taxa
of the species range were detected.
The ratio of the main taxonomic groups (Rotatоria:
Cladocera: Copepoda) (Table 3) of zooplankton community
shows that when taking rotifers prevail in numers
but cladocerans dominate in biomass in all the
stations except in Skrukkebukta where copepods
dominate. Shannon species diversity index by number
varied in the range of 2.1–2.8 bit/individuals and the
highest value was obtained at the Tjerebukta station.
The index value of the average individual zooplankter
mass in the community varied within the range of
0.002 mg (Rajakoski)–0.02 mg (Tjerebukta) and the
mean was 0.008 mg.
The highest abundance was noted at the Ruskebukta
station (239.8 10 3 ind./m 3 ) where the biomass
was 1.8 g wet weight/m 3 . At other sites the total abundance
and biomass were not high and were characteristic
of the oligotrophic water bodies (37.6–76.4 10 3
ind./m 3 and 0.2–0.7 g wet weight/m 3 ). According to the
saprobity index the water of the studied sites characterized
as α-mesosaprobic and in the III water quality
class. According to the Kitaev’s trophic scale the lakes
are α-oligotrophic type except for Ruskebukta, which
is α-mesotrophic type.
65

Conclusions
The phytoplankton species composition in the Pasvik
watercourse is characterized by high diversity and it
is different in different reaches. 95 algae taxa have
been found at the level one order lower than genus.
Relatively high species diversity is typical of the border
area conditions combining water masses different
in hydrodynamic and hydrochemical properties.
Diatoms, blue-green and yellow-green algae were
the most abundant taxonomic groups. Abundance of
green algae separates Lake Kuetsjarvi from other stations.
In Ruskebukta a massive development of the
diatom Urosolenia eriensis was observed, which is a
sign of eutrophication.
The highest chlorophyll a concentrations in 2012
were detected in Ruskebukta and Lake Kuetsjarvi
which correlates with indicators of phytoplankton
abundance and hydrochemical analyses results. According
to chlorophyll a concentration the trophic status
of study areas ranges from α-oligotrophic (Skrukkebukta)
to β-mesotrophic (Kuetsjarvi, Kolosjoki). The
phytoplankton biomass in the Pasvik River areas in
2012 was within 0.23–2.94 g/m 3 which does not exceed
the average values for the Kola Peninsula. The
saprobity index calculated according to phytoplankton
indicators was within 1.27–1.89, which means a relatively
low water pollution level (Water quality classes
II and III).
The many-year dynamics of phytoplankton biomass
in Lake Kuetsjarvi show an increasing trend
as a result of reduction of toxic load and anthropogenic
eutrophication supported by climate warming.
In other water bodies no increase in production has
been found. However, analysis of the collected data
is difficult because of different conditions in each station
and irregular intervals in sampling in different seasons.
Synchronization of sampling at each station is
recommended for collection of representative data.
The species composition and structure of phytoplankton
communities in the Pasvik River has undergone
a number of considerable changes. The previously
dominating diatoms and yellow-green algae are
being replaced by green and blue-green algae which
confirms climate warming and presence of eutrophication
processes.
The ratio of the main taxonomic groups of zooplankton
in total number reflects the prevalence of
Rotatoria, and of the biomass the groups of “fine” and
“coarse” filtrators, which are valuable fish feed. Shannon
species diversity index by the number of zooplankton
has a relatively high value, also characteristic
of the oligotrophic water bodies. It should be noted
that low content of taxa and high quantitative values
at some stations can be explained by multifactorial impact
and by the period of plankton sampling
Zooplankton can be used to evaluate the water
quality. Based on the analysis of qualitative and quantitative
structural and functional indicators a conclusion
can be made that the sites under study of the Pasvik
river-and-lake system (Rajakoski, Tjerebukta, Ruskebukta,
Vaggatem, Skrukkebukta) have an oligotrophic
status and average saprobity of α-mesosaprobic.
The water bodies belong in the III water quality class,
“slightly polluted.” They are of α-oligotrophic type except
for Ruskebukta, which is α-mesotrophic type.
Bloom of blue-green algae. Photo: Juha Riihimäki
66

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Krammer, K. & Lange-Bertalot, H. 1991. Bacillariophyceae, volume 3: Centrales, Fragilariaceae, Eunotiaceae. In: Ettl, H.,
Gerloff, J., Heynig, H. & Mollenhauer, D. (ed.): Süsswasserflora von Mitteleuropa, Band 2. Stuttgart, Gustav Fischer
Verlag, Jena. 576 p. (in German)
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(Lineolatae) und Gomphonema. In: Ettl, H., Gärtner, G., Gerloff, J., Heynig, H. & Mollenhauer, D. (ed.): Süsswasserflora
von Mitteleuropa, Band 2. Stuttgart, Gustav Fischer Verlag, Jena. 437 p. Krammer K. 2000: The genus Pinnularia. In: H.
Lange-Bertalot (ed.), Diatoms of Europe. 1: A.R.G. Gantner Verlag K.G. Vaduz. 703 p. (in German)
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Diatoms of Europe, volume 4. A.R.G. Gantner Verlag K.G.. Ruggell. 530 p.
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67

5 Aquatic macrophytes of Lake Inarijärvi
and the Pasvik River
JUHA RIIHIMÄKI, MARIT MJELDE, SEPPO HELLSTEN
Regulation of the water level of Lake Inarijärvi and the
Pasvik River due to hydropower production is probably
the strongest human induced pressure on aquatic
ecosystem in the Pasvik River basin. The global climate
change will also have several effects on hydrological
cycle; changing the timing of high water levels and
discharges and thus affecting the habitat conditions
of aquatic organisms (see Chapter 3, Climate change
impacts on hydrology and water level fluctuation).
Assessment of the ecological status of Lake Inarijärvi
and the Pasvik River using aquatic macrophytes as
biological elements is important.
Materials and methods
Macrophyte data was collected from lakes Inarijärvi,
Muddusjärvi and Nitsijärvi and the Pasvik River. Lakes
Muddusjärvi and Nitsijärvi are unregulated lakes
and are used as reference lakes. All lakes are classified
as “large oligohumic lakes (North)” in Finnish lake
typology (Aroviita et al. 2012). The lakes in the Pasvik
River are classified as low alkalinity, clear lakes using
Norwegian typology (Direktoratsgruppa 2013).
Field work on macrophyte sampling in the lakes
Inarijärvi, Muddusjärvi and Nitsijärvi was done 31.7.–
15.8.2012. Macrophyte data was gathered using the
Finnish “Main belt transect method” (Kuoppala et al.
2008). Observations of macrophyte species were made
along a 5 m wide transect perpendicular to shoreline.
Starting point for the transects were at the upper
eulittoral and extended to the outer depth limit of the
macrophyte vegetation. All macrophyte species (including
helophytes and bryophytes) were recorded, and
frequency and abundance for each species was estimated
using a continuous percentage scale.
In Lake Inarijärvi 24 transects in 5 areas were surveyed.
Lakes Muddusjärvi and Nitsijärvi had 25 evenly
distributed transects. Total area surveyed differs
among the lakes since the length of transects are determined
by the outer limit of the vegetation on transect.
Total length of transects and hence also the total
area was higher in Lake Nitsijärvi than in the other
two lakes.
The macrophyte survey in the Pasvik River took
place 27.–30. August 2013. Here, the macrophyte
data was collected using both the Finnish and the
Norwegian field methods. The Norwegian method
(Mjelde 2013) includes only true aquatic macrophytes
(i.e. isoetids, elodeids, nymphaeids, lemnids and charophytes).
Helophytes, bryophytes and filamentous
algae are excluded. Different habitats, from shore
to maximum vegetation depth, are surveyed and the
species are recorded using an aquascope and collected
by dredging from a boat. Species abundance
is estimated using a semi-quantitative scale (1=rare,
2=scattered, 3=common, 4=locally dominant and
5=dominant) and maximum depth distribution of vegetation
is noted.
The Norwegian field method was applied also in in
previous macrophyte study in the Pasvik River (Moiseenko
et al. 1993) and the same study sites on the
Norwegian side of the river were used in both surveys.
A total of 15 sites using Norwegian method was
visited. The Finnish field method was applied for 14 of
those sites, with one transect on each site.
Ecological status of the lakes and the Pasvik River
was assessed using macrophytes according the European
Union Water Framework Directive. Assessment
method for Finnish lake macrophytes was used for the
lakes and both Finnish and Norwegian methods were
used for the Pasvik River.
Finnish assessment method is a multimetric index
combining results of three different metrics: Proportion
of type specific taxa (TT50), Percent Model Affinity
(PMA) and Trophic index (RI) (see Vuori et al. 2009,
Aroviita et al. 2012). Observed metric values of studied
lakes are divided by the average metric values of
reference lakes (expected values) to calculate Ecological
Quality Ratio (EQR) for each metric. EQRs are
scaled to common thresholds so that scaled EQR value
0.8 is threshold for high/good status, 0,6 for good/
moderate, 0.4 for moderate/poor and 0.2 for poor/bad.
Norwegian assessment method (TIc index) is
based on the relationship between the number of sensitive
and tolerant species in relation to eutrophication
(Mjelde 2013). EQR is calculated using observed TIc
68

index and expected TIc index value obtained from the
reference lakes.
Results
Lakes Inarijärvi, Muddusjärvi and Nitsijärvi
The total number of observed macrophyte species
in the studied lakes was 45, of which only 18 species
were common to all three lakes, 12 species were
common for two lakes and 15 species were observed
only in one lake. However, the total number of observed
species per lake was quite even: in Lake Inarijärvi
there were 33 species, in Lake Muddusjärvi 31 and in
Lake Nitsijärvi 29.
Classification of true aquatic macrophytes (helophytes
and bryophytes are omitted) according to
their indicator value related to sensitivity and tolerance
against eutrophication (Penning 2008 a, b) showed
very similar composition among the lakes. Eutrophication-tolerant
species were totally missing from all
of the lakes and the species pool was dominated by
eutrophication-sensitive species with only few indifferent
species per lake.
Ecological status of the Lake Inarijärvi was assessed
using Finnish multimetric index for lake
macrophytes. Macrophyte data from lakes Muddusjärvi,
Nitsijärvi, Kitkajärvi and Yli-Kitka were used as
reference data. Average EQR of the three metrics was
0.81, so Lake Inarijärvi was assessed to be slightly in
high ecological status based on aquatic macrophytes.
For each separate metrics the EQR value was also
clearly above good/moderate boundary.
The Pasvik River
Total number of macrophyte species, including true
aquatic macrophytes, bryophytes and helophytes, observed
in the Pasvik River sites was 47. 37 of them
were observed using the Finnish field method and 34
using the Norwegian field method (only true aquatic
macrophytes). The number of species per site was
higher using the Norwegian method in all but one site
(site 4). Average number of species per site using the
Finnish method and the Norwegian method were 11
and 14 and range (min–max) 5–17 and 4–22 species,
respectively.
There is a clear difference in the field methods that
affects the results. In the Finnish method also helophytes
and bryids are observed and the number of
visited sites at the Pasvik River was different. When
comparing results using only common sites and common
observed growth forms, the total number of observed
plant species using the Finnish and Norwegian
field method were 27 and 33 species, respectively.
Number of species per site is clearly higher in all
sites with the Norwegian method when comparison
was made using only common sites and growth forms
(Figure 1). In this comparison the average number
Number of species
25
20
15
10
5
0
New 1 2 3 4 5 6 7 8 9 14 15 18 19
Species per site FI
Species per site NO
Figure 1. Number of species per the Pasvik River macrophyte study site using
the Finnish field method (FI) and the Norwegian field method (NO) with common
growth forms and sites.
69

Table 1. Ecological status assessment of the Pasvik River lakes using the Finnish and Norwegian status assessment methods. Numbers after
lake names indicate site codes. The Finnish method was not applied on site 16.
Data
The Pasvik River (all
sites)
RI TT50SO PMA Total (FI) TIc
EQR Status EQR Status EQR Status EQR Status EQR Status
0.72 Good 0.62 Good 0.66 Good 0.67 Good 0.90 Good
Hestefoss (new) 0.60 Good 0.70 Good 0.12 Bad 0.47 Moderate 1.11 High
Fjørevatnet (1) 0.65 Good 0.47 Moderate 0.06 Bad 0.39 Poor 0.88 Goog
Vaggatem (2,3,4,5,6) 1.13 High 0.87 High 0.55 Moderate 0.85 High 0.89 Good
Langvatn (7,8) 1.00 High 0.70 Good 0.75 Good 0.82 High 0.98 High
Fuglebukta (9) 1.13 High 1.03 High 0.63 Good 0.93 High 1.00 High
Svanvatn (14, 15, 16) 0.74 Good 0.70 Good 0.61 Good 0.68 Good 0.87 Good
Björnvatn (18, 19) 1.13 High 0.70 Good 0.46 Moderate 0.76 Good 0.91 Good
of species per site using the Finnish method and the
Norwegian method were 9 and 15 and range (min–
max) number of species 3–14 and 4–22 species, respectively.
Ecological status assessment gave quite similar results
when Finnish and Norwegian assessment methods
were compared using RI index and TIc index,
and all the Pasvik River lakes were classified to high
or good status (Table 1). Also, when combined Finnish
multimetric index was used, most of the lakes
were classified to high or good status, except Hestefoss
and Fjørevatnet where low number of sites made
PMA index unstable and lowered status (Table 1). Results
showed that relatively similar RI and TIc indices
gave exactly the same results showing relatively high
status of the Pasvik River lakes.
Macrophyte composition was also assessed by
using a water level regulation index developed by
Mjelde et al. (2012). The index showed that all lakes
except Hestefoss and Langvatn were in better than
moderate status. However, this index is developed for
lakes regulated for hydroelectric power with (more or
less) considerable winter drawdown. The lakes in the
Pasvik River have different regulation regimes, with
limited winter drawdown.
Aquatic macrophyte diversity of the Pasvik River is
significantly higher compared to other large rivers in
Norway (excluding River Glomma, which is situated
in southern Norway and represents naturally higher
diversity gradients).
Discussion
The ecological status of Lake Inarijärvi based on
aquatic macrophytes was high. Water level regulation
for hydropower production is considered to be the
dominant human induced pressure to Lake Inarijärvi
since nutrient loading due to human activity are estimated
to be relatively low. Average water level fluctuation
during the period 2000–2009 has been about
1.40 meters, which is about 0.30 meters larger than
the natural water level fluctuation (Puro-Tahvanainen
et. al. 2011). Water level regulation induced effects
on littoral areas at Lake Inarijärvi are limited and the
macrophyte communities are well adapted to the current
conditions. However, it should be noted that vertical
extension of sedges (Carex spp.) has decreased
and also areas of spring-flood depended vegetation
are smaller.
The macrophyte surveys in the Pasvik River lakes
showed similar high-good status in almost all lakes.
Despite the fact that the whole river has changed significantly
and consists of cascades of hydropower
reservoirs, macrophyte species composition resembles
natural. It should be noted that water level of lakes
is relatively stable and without significant winter
drawdown. Winter drawdown is one of the most significant
factors negatively affecting the status of lake
macrophytes, as shown in several studies (Mjelde et
al. 2013, and references herein). On the other hand,
more or less stable water levels (as in the Pasvik River
lakes) positively affect the abundance of several
aquatic macrophyte species. However, abundance of
helophytes and especially sedges is much lower than
70

in lakes with normal spring flood reflecting decreased
water level fluctuation.
The Pasvik River water quality reflects largely the
outflow of Lake Inarijärvi, which in general is in good
status. Therefore, the number species indicating
eutrophication is low even in areas affected by the
Pechenganikel.
Biological monitoring of Lake Inarijärvi and the Pasvik
River using macrophytes is well established and
usable in its current state. Both Finnish and Norwegian
field methods and ecological status assessment
show similar results regardless of the obvious disparities
in the field methods. Aquatic macrophyte surveys
are lacking from the Russian side of the Pasvik River
basin, hence we recommend setting up comparable
macrophyte monitoring and applying the status assessment
system also to the Russian area of the river
basin.
Macrophyte studies in the Pasvik River. Photos: Juha Riihimäki
71

References
Aroviita, J., Hellsten, S., Jyväsjärvi, J., Järvenpää, L., Järvinen, M., Karjalainen S. M., Kauppila, P., Keto, A., Kuoppala, M.,
Manni, K., Mannio, J., Mitikka, S., Olin, M., Perus, J., Pilke, A., Rask, M., Riihimäki, J., Ruuskanen, A., Siimes, K., Sutela,
T., Vehanen, T., Vuori, K.-M. 2012: Ohje pintavesien ekologisen ja kemiallisen tilan luokitteluun vuosille 2012–2013 −
päivitetyt arviointiperusteet ja niiden soveltaminen. (Guidelines for the ecological and chemical status classification of
surface waters for 2012–2013 – updated assessment criteria and their application). Ympäristöhallinnon ohjeita 7/2012,
144 p. (in Finnish)
Kuoppala M., Hellsten S., Kanninen A. 2008: Sisävesien vesikasviseurantojen laadunvarmennus (Development of quality
control in aquatic macrophyte monitoring). Suomen ympäristö 36: 1–93.
EEA 2008: European River Catchments. GIS database. http://www.eea.europa.eu/data-and-maps/data/european-rivercatchments-1
Hellsten, S., Willby, N., Ecke, F., Mjelde, M., Phillips, G., Tierney, D., Poikane S. (ed.). 2014: Water Framework Directive
Intercalibration Technical Report: Northern Lake Macrophyte ecological assessment methods. JRC Technical Reports.
Luxembourg: Publications Office of the European Union., ISBN: 978-92-79-35470-0
Lapin ympäristökeskus 2010: Tenon–Näätämöjoen–Paatsjoen vesienhoitoalueen vesienhoitosuunnitelma vuoteen 2015.
(Teno – Näätämö – Paatsjoki River basin management plan for 2015). 123 p. (in Finnish)
Mjelde, M. 2013. Vannplanter. I: Direktoratsgruppa 2013. Veileder 02:2013 Klassifisering av miljøtilstand i vann (in Norwegian).
Mjelde, M., Hellsten, S., Ecke, F. 2013: A water level drawdown index for aquatic macrophytes in Nordic lakes. Hydrobiologia
704: 141–151
Moiseenko, T., Mjelde, M., Brandrud, T., Brettum, P., Dauvalter, V., Kagan, L., Kashulin, N., Kudriavtseva, L., Lukin, A., Sandimirov,
S., Traaen, T.S., Vandysh, O., Yakovlev, V. 1994: Pasvik River Watercourse, Barents Region: Pollution Impacts
and Ecological Responses. Investigations in 1993. - Institute of North Industrial Ecology Problems (Russia), Norwegian
Institute for Water Research (Norway). NIVA-report OR-3118. 87 p.
Penning, W. E., Dudley, B., Mjelde, M., Hellsten, S., Hanganu, J., Kolada, A., van den Berg, M., Maemets, H., Poikane, S.,
Phillips, G., Willby, N., Ecke, F. 2008a: Using aquatic macrophyte community indices to define the ecological status of
European lakes. Aquatic Ecology 42: 253–264.
Penning, W. E., Mjelde, M., Dudley, B., Hellsten, S., Hanganu, J., Kolada, A., van den Berg, M., Maemets, H., Poikane, S.,
Phillips, G., Willby, N., Ecke, F. 2008b: Classifying aquatic macrophytes as indicators of eutrophication in European lakes.
Aquatic Ecology 42: 237–251.
Puro-Tahvanainen, A., Aroviita, J., Järvinen, E. A., Kuoppala, M., Marttunen, M., Nurmi, T., Riihimäki, J., Salonen, E. 2011:
Inarijärven tilan kehittyminen vuosina 1960–2009, (Development of the status of Lake Inari between 1960 and 2009).
Suomen ympäristö 19/2011, 89 p. (in Finnish, with abstracts in Swefish and in English)
UNECE 2011: Second Assessment of transboundary rivers, lakes and groundwaters. EDE/MP.WAT/33, United Nations.
Vuori, K., Mitikka, S., Vuoristo, H. 2009: Pintavesien ekologisen tilan luokittelu (Guidance on ecological classification of surface
waters in Finland. Part 1: Reference conditions and classification criteria, Part 2: Environmental impact assessment).
Ympäristöopas 3/2009, 120 p. (in Finnish)
Taking a break in Lake Muddusjärvi. Photo: Juha Riihimäki
72

6 Zoobenthos of Lake Inarijärvi and the
Pasvik River
MIKKO TOLKKINEN, EMMANUELA DAZA SECCO, JUKKA AROVIITA, SVETLANA VALKOVA
Alteration of natural water level regime due to
construction of dams and reservoirs for hydropower
production and flood control is one of the major
anthropogenic disturbances in freshwater ecosystems
(Dynesius & Nilsson 1994, Coops et al. 2003). The
main effects of water level regulation are shore-line
erosion caused by raised water level, changes in the
annual water dynamics and changes in mean open
water level (Hellsten 1998). Water level modifications
are likely to be exacerbated in future by global climate
change because of greater frequency of floods and
droughts (Dudgeon et al. 2006). Altered variation of
water level impacts especially the littoral zone of lakes
where organisms, both zoobenthos and fish, can
be affected directly by desiccation and indirectly by
a reduction in habitat availability and food resources
(e.g. Gasith & Gafny 1990).
Lake Inarijärvi belongs to the Pasvik River water
system in Finnish Lapland. Inarijärvi is a subarctic oligotrophic
lake which has been regulated in the Pasvik
area for hydropower production since 1941, with
a yearly water level fluctuation of ~1.38 m (Palomäki
& Hellsten 1996, Hellsten et al. 1997). Regulation
activities have caused coastal erosion and structural
changes in aquatic vegetation and zoobenthic and
fish communities (Hellsten et al. 1997).
This report presents the status of zoobenthic communities
of rocky shores and soft bottom habitats in
Lake Inarijärvi in September 2012 and in the Pasvik
River 2013. Report also summarises the results from
earlier monitoring of the same sites. In addition, unregulated
nearby lakes Nitsijärvi and Muddusjärvi were
sampled in 2012 for regional references to more accurately
evaluate the status of the zoobenthic communities
in the Pasvik River catchment.
Materials and methods
Zoobenthos was sampled from lakes Inarijärvi, Nitsijärvi
and Muddusjärvi in 2012 and the Pasvik River
in 2013 (Introduction, Figure 1). Both soft bottom and
rocky shore habitats were sampled. Samples were
sieved through a mesh of 0.5 mm and preserved
with 70 % ethanol. Zoobenthic animals were identified
to genus or species in the laboratory; Oligochaeta,
Nematoda, Hydracarina and Chironomidae were
counted but not identified.
This report also includes the previous decades-old
data from the same sites as a baseline for temporal
comparisons. Lakes Nitsijärvi and Muddusjärvi were
chosen to reduce latitudinal variation in reference
zoobenthic communities (Jacobsen et al. 1997, Sandin
& Johnson 2000).
Structural and functional indicators of benthic communities
are used as criteria for the evaluation of water
quality as well as comparing the states of communities
and ecosystems with industrial influences (Makrushin
1984, Balushkina 1987, Shitikov et al. 2003, Semenchenko
2011, Zinchenko 2011). Zoobenthos is used to
assess of the state of aquatic ecosystems and water
quality as it is the most long-lived component of community
reflecting the state over a long period of time
and describing its “average” regime. The most widely
used indicators are the total abundance (ind./m 2 ), the
total biomass (g/m 2 ), the total number of species, the
proportion of widespread species in the community,
the indicator species of saprobity, the abundance of
the main groups (ind/m 2 ) and the biomass of the main
groups (g/m 2 ). Kitaev’s trophic scale (1984) classifies
types of lake ecosystems based on zoobenthic communities’
quantitative indicators (Table 1).
Goodnight and Whitley oligochaetic index is based
on the accounting ratio of the number of oligochaetes
and other zoobenthic animals. Woodiwiss biotic index
values are determined in the biotic indices according
to a special table. Both indicators are used to evaluate
the water quality and to control of the environmental
compartments’ pollution (Table 2).
In the Pasvik River and Lake Kuetsjarvi both soft
bottom and rocky shore habitats were sampled in
August–September 2012–2013. Samples were fixed
with 4 % formalin or 70–80 % alcohol. The analysis
of benthic samples was performed and invertebrates
73

Table 1. Kitaev’s trophic scale based on benthic biomass (1984).
Characteristic
Type of lake ecosystem
Oligotrophic Mesotrophic Eutrophic
Hypereutrophic
α β α β α β
Benthic biomass, g/m 2 < 1.25 1.2–2.5 2.5-5 5-10 10-20 20-40 > 40
Table 2. Classification of water quality in ponds and streams in terms of zoobenthos (GOST 17.1.3.07-82).
Water quality
class
Degree of water pollution
Goodnight and Whitley
oligochaetic index, %
Woodiwiss biotic
index, scores
The saprobity zone
I Extremely clear 1 – 20 10 Oligosaprobic
II Clear 21 – 35 7 – 9 Oligosaprobic
III Moderately polluted 36 – 50 5 – 6 β-mesosaprobic
IV Polluted 51 – 65 4 α-mesosaprobic
V Extremely polluted 66 – 85 2 – 3 Polysaprobic
Isoperla sp. Photo Mira Grönroos
Micrasema gelium.
Photo Mira Grönroos
Benthic macroinvertebrate research.
Photo Mirkka Hadzic
74

were identified and their biomass was calculated as
wet weight.
Community data is presented as total abundances
(sum of individuals/site) for rocky shore habitat and
as densities (individuals/m²) for soft bottoms. To summarize
the variability in community structure among
the last three sampling years (2003, 2008, and 2012),
a Non-metric Multidimensional Scaling (NMS based
on Sørensen´s distance) was performed for Lake Inari
community data from both habitats. NMS was also
used to summarize the variability in rocky shore
community structure among regulated Lake Inarijärvi
and the Pasvik River and unregulated lakes Nitsijärvi
and Muddusjärvi. Indicator species analysis (Dufrene
& Legendre 1997) was done to distinguish the species
that best explained the possible difference between
lake groupings.
The community composition was evaluated with
occurrence of Type-specific Taxa (TT; Aroviita et al.
2008) and relative abundance was evaluated with
the Percentage Model Affinity (PMA; Novak & Bode
1992). Data from unregulated reference lakes Nitsijärvi
and Muddusjärvi were used to define the reference
communities and to calculate the expected values,
and to further calculate the Ecological Quality Ratio
(EQR). The sampling effort of the reference lakes
was standardized with Inarijärvi. Status class boundaries
for each parameter were defined by using the
25 th percentile of the reference lakes’ EQR distribution
as high-good quality class boundary. The lower quality
classes good, moderate, poor and bad were then
defined between the high-good class boundary and
EQR = 0 at equal class widths.
Results
Taxa composition and abundance
Rocky shore communities
The number of taxa and individuals in all samples in
Lake Inarijärvi was 42 and 2204 respectively. Highest
taxa richness and individuals’ abundance was found in
Lake Muddusjärvi (Table 3). In average taxa richness
per site was higher in the Pasvik River than in the lakes.
After rarefaction to 71 individuals per site mean
number of taxas per site was 10 in the Pasvik River,
8 in Inarijärvi, 9 in Nitsijärvi and 12 in Muddusjärvi. In
the Pasvik River number of taxa varied between different
river sections. Species richness was highest in
Vaggatem and Svanevatn (37, 25, respectively) and
lowest in Hestefossdammen and Skrukkebukta (19,
both). The zoobenthos consisted mainly of Chironomidae
and Oligochaeta in all sampled water bodies.
There are some differences between study years
(2003, 2008, 2012) in taxa richness and individuals’
abundance. Chironomidae and Oligochaeta were the
most abundant groups among the invertebrate communities
comprising more than 50 % of the total abundance.
17 major groups were identified in the three
sampled years in rocky shores in Lake Inarijärvi. Zoobenthos
abundance (individuals per 30 kicknet samples
from 10 sites) was highest in 2008 and lowest in
2012.
Soft bottom communities
In 2012 32 identified taxa were collected from Lake
Inarijärvi (2 m depth), with an estimated density of
1746 ind./m² and an average of 9 taxa/site (5–15). 13
Table 3. Total, average (per sites and per sample) and range (min–max) number of zoobenthic taxa and individuals of
rocky shores from lakes Inarijärvi, Nitsijärvi and Muddusjärvi from September 2012.
Total Average site Average sample
Lake Taxa Individuals Taxa Individuals Taxa Individuals
Inarijärvi 42 2204 13
(8-24)
200
(76-435)
8
(3-13)
73
(26-177)
Nitsijärvi 22 484 17
(14-19)
161
(108-202)
8
(5-11)
54
(23-81)
Muddusjärvi 34 835 19
(14-24)
278
(221-370)
11
(4-20)
93
(24-253)
the Pasvik River 64 3450 25
(19-37)
459
(212-749)
12
(4-22)
139
(12-393)
75

taxa were collected from Lake Nitsijärvi, where the
density was 773 ind./m² and an average of 7 taxa/site
(4–19). In Muddusjärvi 19 identified taxa were found
and on average density was of 925 ind./m² and 9 taxa/
site. A total of 15 major groups were found. Chironomidae
constituted more than 50 % of the total community.
All major groups were found in Inarijärvi while
Isopoda, Plecoptera, and Heteroptera were not found
in any of the reference lakes. Gastropoda densities
were especially lower in Inarijärvi compared to those
in the reference lakes.
The temporal follow-up of Lake Inarijärvi zoobenthic
communities based on all available data from
literature and this study showed that the highest densities
in soft bottoms were found in 1977 with 2217
ind./m². In average the lowest densities were observed
1965 and 1966 (217 ind./m²). Communities were
mainly composed of chironomids and oligochaetes in
all studied years. Mean densities were lower for the
reference lakes. Sampling effort and method differences
exist among the studies.
Community structure and status
In NMS ordination samples from the Pasvik River
grouped separately from other lakes, and communities
from different river sections in the Pasvik River
clustered together indicating within-river variation. In
Lake Nitsijärvi Chelifera spp. was the only significant
indicator species. In Lake Muddusjärvi Hydrachnellae,
Tipula spp. and Oligochaeta were significant indicator
species whereas Asellus aquaticus was the only
indicator species in the Pasvik River. There were no
indicator species in Lake Inarijärvi.
In NMS ordination samples of the rocky shore community
composition, year 2003 differed quite clearly
from years 2008 and 2012. Communities in 2008 and
2012 appeared more widely distributed in the ordination
space, suggesting larger differences among the
sites. In the soft bottom communities none of the years
clustered clearly. In some cases, regardless of the
sampling year, sites closely located clustered in the
graph suggesting a within lake variation in the communities
associated to the location of the sites. Generally
no clear trend in time for either of the habitats
was observed.
Zoobenthic communities’ status in Lake Inarijärvi
was calculated using subarctic lakes’ communities
as reference. Status assessment of macroinvertebrate
communities of Inarijärvi gave different results
from the two parameters analysed. PMA indicates
that most of the communities from rocky shore and
soft bottom from different years fall into the moderate/
poor class except for the rocky shore community from
2012. Results from TT showed that community status
class was good or moderate in most cases. TT index
indicated generally good status class, whereas PMA
indicated moderate status in most cases (Table 4).
Table 4. Status of zoobenthic communities from Lake Inari based on reference data from unregulated lakes Nitsijärvi and Muddusjärvi.
Lake Inarijärvi values were calculated using data from three randomly selected sites (P1, K4, and L4). Community status was
classified as: high (blue), good (green), and moderate (yellow). None of the index values were found within poor or bad class.
Nitsijärvi and Muddusjärvi (n=2)
Lake Habitat Year TT 0.4
PMA EQRs (TT 0.4
) EQRs (PMA)
Inarijärvi Rocky shore 2003 18 0,38 0,64 0,52
Rocky shore 2008 27 0,39 0,96 0,53
Rocky shore 2012 21 0,45 0,75 0,61
average 0,79 0,55
Soft bottom 1977 7 0,37 0,44 0,49
Soft bottom 2003 13 0,35 0,81 0,46
Soft bottom 2008 10 0,34 0,63 0,45
Soft bottom 2012 12 0,33 0,75 0,44
average 0,66 0,46
Nitsijärvi Rocky shore 2012 22 0,73 0,78 1,00
Soft bottom 2012 12 0,75 0,81 1,00
Muddusjärvi Rocky shore 2012 34 0,73 1,21 1,00
Soft bottom 2012 19 0,75 1,18 1,00
76

Status of zoobenthos of Lake
Kuetsjarvi and the Pasvik River
Zoobenthos of the rocky littoral zone of the Pasvik
River was investigated at Lake Kuetsjarvi, Vaggatem
and Rajakoski. Amphibiotic insects form the basis of
benthic communities of all of the investigated stations,
among which caddisflies and chironomids have the
highest species diversity. The taxonomic diversity of
benthos and the number of indicator groups increase
with distance from the source of pollution.
The Shannon index (bit/ind) is 3.28 in Kuetsjarvi,
1.92 in Vaggatem and 3.35 in Rajakoski. In terms of
saprobity Lake Kuetsjarvi is β-mesosaprobic whereas
the other lakes are oligosaprobic. Kuetsjarvi belongs
to the III water quality class, “moderately polluted”,
and the Woodiwiss index varies between 6–7. Vaggatem
and Rajakoski belong in the II class, “clear”, and
the Woodiwiss index values are 7 and 8, respectively.
Variety of zoobenthos in the profundal soft bottoms
of Lake Kuetsjarvi and the Pasvik River is low. 4
systematic groups of invertebrates were identified in
the samples. Chironomids (predominantly Procladius
choreus gr.) and the oligochaetes dominate in benthic
communities of the Pasvik River at all stations. The other
groups of zoobenthos (mainly bivalves and water
mites) are rare. The quantitative indicators are low at
all stations (Table 5). The maximum benthos density
was observed at Ruskebukta, which is mesotrophic
whereas the other stations are oligotrophic. The minimum
values ​of numbers and biomass were observed
at Skrukkebukta station, possibly due to greater
depths (> 20 m).
In Lake Kuetsjarvi the profundal zone is characterized
by low taxonomic diversity. Oligochaetes, chironomids
and bivalves form the basis of the communities
and dipterous larvae, caddisflies and water mites
are met sporadically. Quantitative indicators are low.
The number of benthos was on average 506.9 ind./
m 2 and biomass 2.1 g/m 2 with considerable variation
of both indicators in the samples and in different
zones of the water body. Shannon index values are
less than 1 in all sampling stations of the lake, varying
between 0.79–0.98 bit/ind. Oligochaetic index was 42
%, “moderately polluted”, with a variation between the
samples from 20 % to 80 %. The trophic state of water
is rated as oligotrophic, largely due to pollution with
Pechenganikel industrial complex effluents promoting
the “oligotrophysation” processes of the water body
(Yakovlev 2005).
In Lake Kuetsjarvi 18 species of Chironomidae were
identified. The basis of chironomid communities is
formed of Procladius, Cricotopus and Chironomus,
which are common in polluted lakes. 13 species are
found in the deepwater zone of Lake Kuetsjarvi and
three species account for > 70 % of the total number
of chironomids: Sergentia coracina, a coldwater species
widespread in the deepwater zones of various lakes
of Murmansk Region, and Chironomus cingulatus
and Prodiamesa olivacea, which are resistant to water
pollution with heavy metals. 9 species of chironomids
were found in the littoral zone of Lake Kuetsjarvi: the
basis of communities is formed from the Orthocladiinae
subfamily members Cricotopus silvestris gr. and
Procladius choreus gr., which are commonly found in
polluted streams.
Discussion
Variation in environmental variables caused by lakes’
geographical position (latitude) generally strongly influences
community structure and composition (e.g.
Jacobsen et al. 1997; Sandin & Johnson 2000).
Zoobenthic communities in the unregulated subarctic
lakes (Nitsijärvi and Muddusjärvi) differ from
those in unregulated more southern lakes. For further
biomonitoring studies subarctic unregulated reference
Table 5. Composition and quantitative indicators of deepwater zones’ zoobenthos of Lake Kuetsjarvi and some stretches of the
Pasvik River.
Species Kuetsjarvi Ruskebukta Skrukkebukta Vaggatem Tjerebukta
Mean values of number,
ind./m 2 506.0 1211.0 103.8 553.6 276.8
Mean values of biomass,
g/m 2 2.1 4.8 0.4 2.2 1.1
Shannon index,
bit/ind.
0.88 1.37 1.0 0.99 0.95
Trophic state oligotrophic mesotrophic oligotrophic
77

lakes should be sampled together with Lake Inarijärvi
to avoid bias caused by within-year and biogeographical
variability. Additionally, constant monitoring and a
larger set of reference lakes would give more accurate
results for assessment of community status.
In 2012 number of taxa and individuals from rocky
shores from Lake Inarijärvi were on average lower
than those in the unregulated lakes. Littoral zone organisms
are particularly affected by water level regulation
both directly by desiccation and indirectly by a
decrease in habitat availability and food resources
(Gasith & Gafny 1990). Previous studies (Smith et al.
1987, Aroviita & Hämäläinen 2008) have found that
taxa richness decreases with increased regulation
intensity and this effect might be stronger in boreal
lakes due to the exposure of the regulated zone to
subzero temperatures and freezing (Aroviita & Hämäläinen
2008). Temporal follow-up of zoobenthic communities
from rocky shores from Lake Inarijärvi showed
an increase in number of individuals from 2003
to 2008, however, numbers decreased again in 2012.
The small number of sampled years together with natural
variation makes it difficult to yet estimate any
specific effect of water level regulation on rocky shore
macroinvertebrate communities.
In soft bottom communities mean densities were
higher in Lake Inarijärvi than in the unregulated lakes
while average number of taxa did not differ greatly. In
regulated lakes organic matter tends to accumulate
immediately below the drawdown limit which in turn
might increase individuals’ abundance (Palomäki &
Koskenniemi 1993, Furey et al. 2006). Density of soft
bottom zoobenthic communities from Lake Inarijärvi
showed a reduction in 2003 and 2008. The stabilization
of shores and increased sedimentation of organic
matter that has followed water level regulation would
be expected to raise the amount of specific groups
such as Diptera and Oligochaeta, but it has not been
the case in 2003 and 2008. This reduction in density
can be attributed to natural among-year variation
in environmental conditions such as the notably dry
summer of 2003.
The results from TT and PMA from rocky shores
and soft bottom habitats indicate that the status of
the communities in Lake Inarijärvi falls between good/
moderate and poor/moderate conditions respectively.
The discrepancies found between the two indices
might be attributed to the small number of reference
lakes which affects their accuracy. However, both indices
indicate that communities are affected by water
level regulation.
The Pasvik River water bodies are regarded as
oligotrophic except for mesotrophic Lake Kuetsjarvi.
Saprobity index indicated low pollution level, except
for Lake Kuetsjarvi, which was β-mesosaprobic. Water
quality classes were either II or III for the studies
lakes.
Zoobenthic communities in unregulated subarctic
lakes clearly differed from the regulated Pasvik River.
Water level regulation and variation in environmental
conditions caused by elevation may impact community
structure, but the Pasvik River macroinvertebrate
communities contain typical river taxas and therefore
comparison to subarctic reference lakes may not be
suitable.
Despite water level regulation, species richness
and abundance in the Pasvik River was higher than
in the reference lakes. This can be explained by the
water level changes in the Pasvik River being rather
moderate during the year, as in some studies of regulated
lakes the benthic macroinvertebrate taxa
richness decreases only beyond 2.0 m amplitude disturbance
level (White et al. 2011). Abundance of the
most sensitive species to water level changes (e.g.
Polycentropus flamoculatus, Sialis sp. and Caenis horaria
(Ephemeroptera)) varied between sampling sites,
indicating that some parts of the Pasvik River may
be more sensitive to water lever changes than others.
Minimum water level fluctuation is recommended.
78

References
Aroviita, J., Hämäläinen, H. 2008: The impact of water level regulation on littoral macroinvertebrate assemblages in boreal
lakes. Hydrobiologia 613: 45–56.
Aroviita, J., Koskenniemi, E., Kotanen, J. & Hämäläinen, H. 2008: A priori typology-based prediction of benthic macroinvertebrate
fauna for ecological classification of rivers. Environmental Management 42: 894–906.
Balushkina, E.V. 1987: The functional significance of chironomid larvae in continental waters. Nauka. Leningrad.179 p. (in
Russian)
Coops, H., Beklioglu, M., Crisman, T. L. 2003: The role of water-level fluctuations in shallow lake ecosystems– workshop
conclusions. Hydrobiologia 506: 23–27.
Dudgeon, D., Arthington, A. H., Gessner, M. O., Kawabata, Z. I., Knowler, D. J., Lévêque, C., Sullivan, C. A. 2006: Freshwater
biodiversity: importance, threats, status and conservation challenges. Biological reviews 81(2), 163–182.
Dynesius, M., Nilsson, C. 1994: Fragmentation and flow regulation of river systems in the northern third of the World. Science
266: 753–762.
Furey, P. C., Nordin, R. N., Mazumder, A. 2006: Littoral benthic macroinvertebrates under contrasting drawdown in a reservoir
and a natural lake. Journal of the North American Benthological Society 25: 19–31.
Gasith A., Gafny, S. 1990: Effects of Water Level Fluctuation on the Structure and Function of the Littoral Zone. In: Tilzer M.
M. & Serruya C. (eds). Large Lakes Ecological Structure and Function. Springer-Verlag, Berlin: 156–171.
Hellsten, S.K. 1998: Environmental factors related to water level regulation- A comparative study in northern Finland. Boreal
Environmental Research 2: 345–367.
Hellsten, S., Palomäki, R., Järvinen, E. 1997: Inarijärven vedenkorkeuden säännöstelystä ja sen vaikutuksista rantavyöhykkeellä.
Lapin ympäristökeskuksen moniste 2: 1–77.
Jacobsen, D. Schultz, R., Encalada, A. 1997: Structure and diversity of stream invertebrate assemblages: the influence of
temperature with altitude and latitude. Freshwater Biology 38: 246–261.
Kitaev, S.P. 1984: Ecological basis of lakes bioefficiency of various natural zones. Nauka. Moscow. 309 p. (in Russian)
Makrushin, A.V. 1984: Bioindication of inland water bodies pollution. Biological methods of natural waters evaluation.
Nauka. Moscow. 123-137. (in Russian)
Novak, M.A., Bode, R.W. 1992: Percent Model Affinity - A new measure of macroinvertebrate community composition. Journal
of the North American Benthological Society 11: 80–85.
Palomäki, R., Hellsten, S. 1996: Littoral macrozoobenthos biomass in a continuous habitat series. Hydrobiologia 339:
85–92.
Palomäki, R., E. Koskenniemi, 1993: Effects of bottom freezing on macrozoobenthos in the regulated Lake Pyhäjärvi.
Archiv fur Hydrobiologie 128: 73–90.
Sandin, L., Johnson, L.K. 2000: Ecoregions and benthic macroinvertebrate assemblages of Swedish streams. Journal of
the North American Benthological Society 19: 462–474.
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Science. Minsk. 329 p.
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of Ecology of the Volga River Basin RAS. Togliatti 463 p.
Smith, B. D., Maitland, P. S., Pennock, S. M. 1987: A comparative study of water level regimes and littoral benthic communities
in Scottish lochs. Biological Conservation 39: 291–316.
White, M.S., Xenopoulos, M.A., Metcalfe, R.A., Somers, K.M. 2011: Water level thresholds of benthic macroinvertebrate
richness, structure, and function of boreal lake stony littoral habitats. Canadian Journal of Fisheries and Aquatic Sciences
68: 1695-1704.
Yakovlev, V.A. 2005: Freshwater zoobenthos of Northern Fennoscandia (Variety, structure and anthropogenic dynamics).
Kola Science Center RAS 1. Apatity 161 p. (in Russian)
Zinchenko, T.D. 2011: Ecological and faunistic characteristics of chironomids (Diptera, Chironomidae) of the small rivers of
the middle and lower Volga (Atlas). Togliatti. (in Russian)
Rhyacophila nubila.
Photo Mira Grönroos
Asellus aquaticus.
Photo Mira Grönroos
79

7 Fish communities of the Pasvik River
and long-term malformation tendencies
PETR TERENTJEV, NIKOLAY KASHULIN, ELENA ZUBOVA, MIKKO TOLKKINEN
Long-term changes in the structure of the community
and changes in the population and organisms were
estimated taking into account the anthropogenic load
intensity in different areas at different times. Data of
multiyear investigations covering a long period (from
the early 1990s till the present) were analyzed. The
researched water bodies (lakes) were Lake Kuetsjarvi
and water storage reservoirs Rajakoski (Russia), Vaggatem
and Skrukkebukta (Norway). The fish fauna
consists of representatives of 9 species belonging to
8 families: trout (Salmo trutta L.), European whitefish
(Coregonus lavaretus L.), European vendace (Coregonus
albula L.), European grayling (Thymallus thymallus
L.), pike (Esox lucius L.), burbot (Lota lota L.),
perch (Perca fluviatilis L.), Eurasian minnow (Phoxinus
phoxinus) and nine-spined stickleback (Pungitius
pungitius). The lakes form a gradient of anthropogenic
load relative to their distance from the Pechenganikel
smelter.
Malformations are found in fish living in polluted
conditions and exposed to various chemical substances.
Detected malformations include changes in external
appearance (pigmentation of integument, depigmentation
of skull), spinal curvature (i.e. scoliosis,
ithykyphosis, lordosis), malformed gills (deformed, bifurcated
and club-shaped rakers, irregular row or partial
lack of rakers, onset of necrotic abnormalities in
gill filament tips (anaemic ring)), malformed gonads
(synchronic and asymmetric maturation, constriction
and torsion of gonads), malformed liver (destruction of
tissue, hyperaemia, focal necrosis resulting in change
in color and elongation) and malformed kidneys (hyperemia,
hemorrhages, progression of focal necrosis,
dystrophic changes in tubule epithelium, onset of granulosis).
The most common kidney disorder is development
of excess connective tissue.
Material and methods
Studies of the fish communities were conducted along
the Pasvik watercourse in the four main localities. Aerial
pollution, sewage of the Pechenganikel enterprise
and domestic sewage flow into Lake Kuetsjarvi, located
ca 5 km from the Pechanganikel and downstream
the Pasvik River. Skrukkebukta is located 16 km northwards
of the Pechenganikel smelter. Vaggatem is located
upstream from the Pechenganikel as is Rajakoski,
farthest away (Introduction, Figure 1).
Sampling was done with a standard set of bottom
nets. The nets were employed in the littoral zone one
by one perpendicular to the coast and in the profundal
zone there were ten ormore nets in line. Age of
fishes was determined from the operculum, cleithrum
or scales (method of Pravdin 1960,1966). The rakers
on the first gill arch of the whitefishes were counted
to isolate intraspecific forms (Pravdin 1966, Reshetnikov
1980, Amundsen et al. 2004, Siwertsson et al.
Figure 1. Rakers on
the first gill arch of SR
whitefish with 23 rakers
(left) and DR whitefish
with 34 rakers (right)
in Lake Kuetsjarvi in
2012–2013.
80

2008) (Figure 1). Back-calculations of growth were
carried out using the formula of Lee (Chugunova
1959, Bryuzgin 1969). The formula for back calculations
of body length for sparsely-rakered (SR) whitefish
of Lake Kuetsjarvi was: lnL i
= 71.38 + R i
/ R ×
(lnL n
– 71.38) and for densely-rakered (DR) whitefish
of Lake Kuetsjarvi: lnL i
= ln25.64 + lnR i
/ lnR × (lnL n
– ln26.64). The formula for back calculations of body
length for hybrid whitefish of Rajakoski: lnL i
= ln39.15
+ lnR i
/ lnR × (lnL n
– ln39.15). The specific growth rate
was calculated using the formula of Schmalhausen-
Brodie (Schmalhausen 1935, Mina & Klevezal 1976,
Dgebuadze 2001).
Heavy metals in skeletal muscle tissues, liver, kidney
and gills 10–15 fish individuals of the same size
were analyzed. The metal concentration was expressed
in mg/g dry weight.
The Pasvik River littoral fish were sampled in September
2013 by electrofishing. Sampling sites were
habitats that have shelters to fish (stony littoral or
macrophytes). All captured fish were identified and
counted. Total length of every fish was measured and
pooled individuals of each species were weighted.
Presented fish densities represent the catch of one
electrofishing run.
Results
Fish communities and population
characteristics
Lake Kuetsjarvi
Despite the intensive industrial pollution all 9 fish species
are present. The structure of fish population has
not changed profoundly during the last 20 years (Figure
2). Predominant species are whitefish and perch;
the rest composed less than 1 %. In the conditions
of the highest anthropogenic load and toxic environment
of Lake Kuetsjarvi whitefish is probably the most
stable species capable of maintaining relatively high
population by early maturing, change of life cycle strategy
and polymorphism (Kashulin et al., 1999). The
share of whitefish in the samples changed from 75
% to 96 %. The population of whitefish in Kuetsjarvi
Lake was presented by two forms, sparsely rakered
(SR) and densely rakered (DR), which was more numerous.
Sparsely-rakered whitefish of the lake can be
divided into fast-growing (large, LSR) and slow growing
(small, SSR) forms. Ecological niches of these
forms of whitefish do not intercross. High water body
trophicity creates favorable conditions for co-existence
of several forms of whitefish. The vendace of Lake
Kuetsjarvi are represented by one species.
The average weight of DR whitefish during the
whole period of studies varied widely (23–82 g), and
the length varied from 12.1 to 18.7 cm. In some years
the average values of size and weight increased,
which probably was caused by large fish migrating into
the water body from other parts of the Pasvik River.
Maximum age of DR whitefish was eight years (Figure
7). Juvenile individuals composed 2.9 % of the community.
Extremely early maturing (age 1+) of individuals
was registered in both DR and SR whitefish. The
share of fish ready for spawn, overall in the population
in the pre-spawning period, varied from 20 % to 66
%. Minimal size of DR whitefish spawning for the first
time was the weight of 6 g and the length of 9.5 cm.
The average weight and length of SR whitefish
varied from 38 to 140 g and from 14.7 to 20.5 cm.
Length-weight parameters did not significantly change
during more than 20 years. The largest individuals
of SR whitefish were aged eleven years (Figure 7).
Juvenile individuals composed < 8.5 % of the community.
SR whitefish reach maturity at the age 1+ with
the weight 8 g and the length 9.5 cm. Overall in the
population percentage of fish ready for spawning varied
from 24 % to 49 %.
Growth rates of both whitefish morphs have decreased,
which can be explained by persisting anthropogenic
load.
Skrukkebukta
Whitefishes are predominant in the community structure
and the proportion of whitefish in the selection during
the whole study period varied from 64.2 to 77.5%
(Figure 4). Proportions of vendace and perch were
19–28 % and the proportionst of other species varied
from 0.1 to 2.4 %. The proportion of perch increased
during the study.
The average weight of DR whitefish was 18–42
g and length 11.5–15.5 cm. Age composition of DR
whitefish was dominated of fish of young age classes
(0+–1+) (Figure 7) but in 2004 individuals aged 1+,2+
and 4+ were represented almost in equal proportion.
Size of DR whitefish spawning for the first time was
less than 6 g and 8.9 cm. In all age groups there was
a high number of non-spawning fish.
SR whitefish were presented by individuals of small
size. The average weight and length were 50–150 g
and 14.8–19.5 cm. The basis of the population was
81

1990-1992
1998
2004
perch
10%
pike
11%
burbot
0,2%
grayling
0,2%
perch 1% pike 1%
burbot
2%
vendace
6.5%
perch
9%
pike
2%
grayling 0,2%
trout
1%
whitefish 78%
whitefish 96%
whitefish 83%
Figure 2. Proportion
of fish species
in Lake Kuetsjarvi
in different periods
of the research.
2007
2009
2012-2013
perch
24%
pike 0%
burbot 5% vendace 1%
perch 0.8%
burbot 1%
pike 3%
perch 8%
grayling 5%
trout 1%
trout
0.8%
whitefish 75%
whitefish 93%
whitefish 82%
1990-th
2000-th
2010-th
Figure 4. Proportion
of fish species
in Skrukkebukta in
different periods of
the research.
pike 1.0%
perch 7.1%
burbot
1.7%
vendace
11.9%
trout
0,1%
whitefish 77%
grayling 1.0%
trout 0,1%
grayling 1.8%
pike 2.4%
perch 9.3%
burbot 2.1%
vendace
18.8%
whitefish 64.2%
pike 0.5%
perch 16.7%
burbot
0.6%
vendace
9.0%
trout 0,1%
grayling 1.9%
whitefish 70.5%
1990-th
2000-th
2010-th
Figure 5. Propotion
of fish species in
Vaggatem in different
periods of the
research.
perch
25%
burbot
1%
pike 4%
vendace
33%
whitefish
37%
perch
30%
burbot
0.4%
pike
4.4%
trout 0.2%
grayling 0.2%
whitefish
28.1%
vendace
29.6%
perch
24.2%
burbot
0.1%
pike 6.5%
trout 0,1%
grayling
0.3%
vendace
32.4%
whitefish
36.4%
2002
2004
2012
Figure 6. Proportion
of fish species
in Rajakoski n different
periods of the
research.
pike 3%
perch 4%
burbot
28%
grayling 1%
whitefish
41%
perch
50%
pike 6%
grayling 1%
whitefish
41%
pike 8%
perch
32%
trout 4%
grayling 2%
whitefish
47%
82
vendace
23%
vendace 1%
burbot 1%
burbot 6%
vendace 1%

No of obs
120
100
80
60
40
20
No of obs
70
60
50
40
30
20
10
Kuetsjarvi
0
0+
1+
2+
3+
4+
5+
6+
7+
8+
10+
9+ 11+
0
0+
1+
2+
3+
4+
5+
6+
7+
8+
9+
10+
11+
No of obs
40
35
30
25
20
15
10
5
0
30
25
20
15
10
5
0
0+ 2+ 4+ 6+ 8+ 10+ 12+ 14+
1+ 3+ 5+ 7+ 9+ 11+ 13+
No of obs
35
0+ 2+ 4+ 6+ 8+ 10+ 12+ 14+
1+ 3+ 5+ 7+ 9+ 11+ 12+
No of obs
22
20
18
16
14
12
10
8
6
4
2
0
30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0
Skrukkebukta
0+ 2+ 4+ 6+ 8+ 10+ 12+ 14+
1+ 3+ 5+ 7+ 9+ 11+ 13+
No of obs
Vaggatem
0+ 2+ 4+ 6+ 8+ 10+ 12+ 14+
1+ 3+ 5+ 7+ 9+ 11+ 12+
DR Whitefish
SR Whitefish
No of obs
30
28
26
Rajakoski
24
22
20
18
16
14
12
10
8
6
4
2
0
0+ 2+ 4+ 6+ 8+ 10+ 12+ 14+
1+ 3+ 5+ 7+ 9+ 11+ 12+
SR Whitefish
Figure 7. The present age structure of DR and SR whitefish of the investigated reservoirs (blue = fish ready to spawn).
83

formed by individuals of older age groups, which is
more common in natural water bodies (Figure 7). It is
obvious that in Skrukkebukta, despite its location in
the lower reaches of the Pasvik River, influence of direct
and aerial pollution is less intensive compared to
Kuetsjarvi Lake. However, early maturing of fish was
observed here as well.
SR whitefish aged fifteen years were encountered
singly. Juvenile individuals (0+–3+) in the selection
comprised from 10 % to 27.8 %. As for DR whitefish,
minimal sizes of maturing fish were extremely small,
the weight of 7 g and the length of 9.4 cm, and maturation
age did not exceed 1+. The percentage of mature
fish ready for spawn was low and varied within
13.8–32.3 % during the whole period of the study.
Analyses of size-age parameters of SR whitefish of
Skrukkebukta showed that the growth rate of fish has
decreased.
Vaggatem
Vaggatem is located ca 40 km upstream from the
Pechenganikel. During the whole period of studies domination
of whitefish was observed in the structure of
the community. However, whitefish and vendace were
encountered here almost in equal proportion. Perch is
also of great importance and its share in samples may
reach 30 % (Figure 5). The proportion of other species
does not exceed 5–7 %.
The basis of DR whitefish population was formed
by individuals with weight up to 100 g and length of
5–18 cm. The age structure of DR whitefish was dominated
by age classes 2+– 4+ and the age of the largest
fish did not exceed ten years (Figure 7). Juvenile
individuals were encountered only in the samples of
2002 (8 %). Early maturing of whitefish is also distinctive
of Vaggatem: individuals of the age 2+, the weight
of 28 g and the length 15.4 cm can be mature. Generally
percentage of fish ready for spawning comprised
46.5 % of the mature population.
The average weight of SR whitefish was 200-420 g
and length 23–31 cm. There were two peaks in length
distribution of fish: 15–25 cm and 30–35 cm. A generally
uniform age distribution of all classes from 1+ to
10+ was seen and larger individuals of fifteen years
were registered singly (Figure 7). SR whitefish begin
to spawn in the age of 2+ with the weight 34 g and the
length 15.3 cm. Quite high percentage (up to 67 % in
2+ or older fish) skips the spawn.
There seems to be an increasing trend in growth
rate of both whitefish morphs, which is the opposite
of the situation of Lake Kuetsjarvi and Skrukkebukta.
Rajakoski
Rajakoski is located ca 65 km upstream from the
Pechenganikel. Whitefish dominates in the fish community
and the share of vendace has significantly
reduced. The proportion of perch varies but there is
generally an increase (Figure 6).
During the research DR whitefish differed by sizeweight
parameters: variation of the weight was 7–548
g and lenght 9.2–36.0 cm. In 2002 the basis was formed
by fish of age 0+ and later the number of individuals
of older age still gradually reduced. There are
no conditions for reproduction in Rajakoski and DR
whitefish may be migrants from the upper reach of
the river and from Lake Inari. Mature individuals were
aged 2+ and minimal maturation weight was 36 g and
length 15.8 cm.
The average weight and length of SR whitefish
grew during the study: in 2002 they were 43 g and
12.4 cm, in 2004 180 g and 22.9 cm and in 2012 585
g and 35.1 cm. The age distribution of SR whitefish
differed greatly between the study years. In 2002 fish
aged 0+ comprised more than 65 % of the population
while fish of other age classes encountered singly but
later the dominating age class changed. This may be
connected to migration of large individuals from the
upper reach of the river as in this area there are no
conditions for reproduction. The percentage of nonspawning
whitefish is very high. Maturity of SR whitefish
comes in the age of 2+ when the weight is 31 g
and the length 14.7 cm.
Rajakoski water reservoir has the highest density
of introduced vendace of all the studied water bodies.
Vendace actively force DR whitefish out of the pelagic
region. They are forced to look for new food items
and habitats intruding the ecological niche of SR whitefish.
This contributes to hybridization of SR and DR
whitefish. In the early 2000’s the dominance of vendace
was significantly lower and segregation of the two
forms of whitefish was more intense.
Fish community of the littoral zone
6 fish species were caught with electrofishing in the
Pasvik River (Figure 8). Rocky shore fish densities
varied between the river sections from 0 to 55 individuals/100
m 2 . Minnows (Phoxinus phoxinus) were
abundant and the only species in Skrukkebukta and
Skogmo sites, which also had the highest fish density.
No fish were caught in Vaggatem-Hauge and Hessefoss.
84

Malformations
Major malformations observed in fish of the studied
lakes are shown in Figures 9–11.
Lake Kuetsjarvi
The most severe malformations are found in Lake
Kuetsjarvi whitefish. The fish have specific kidney
malformations described as progression of nephrolithiasis
(nephrocalcinosis) in response to nickel intoxication.
In some years 100 % of sampled fish had liver
and kidney malformations. Despite lowering anthropogenic
stress there is still no substantial improvement
in the status of fish population in Lake Kuetsjarvi (Figure
12) and whitefish liver and kidney malformation
frequency and rate remains at 75–86 %.
Skrukkebukta
Svanevatn
Melkefoss
Skogmo
Vaggatem -Hauge
Vaggatem
Noatun
Hessefoss
0
20 40 60
Lota lota Phoxinus phoxinus Esox lucius
Salmo trutta Thymallus thymallus Pungitius pungitius
Figure 8. Rocky littoral
shore electrofishing fish
densities/100m 2 .
Figure 9. Depigmentation of whitefish skull (left) and segmentation of gonads of male and female whitefish due to fibrogenesis (right).
Figure 10. Terminal stage of fibrogenesis in whitefish kidney (left) and whitefish kidney stones (right).
85

Vaggatem and Skrukkebukta
Changes in whitefish liver structure, color and shape
were the most common malformation noted in Vaggatem
and other storage reservoirs in early 1990s. Kidney
and gonad-related abnormalities came in second
by frequency of occurrence (Kashulin et al. 1999).
Later the frequency of gonad-, gill- and liver-related
malformations decreased and the frequency of kidney
malformations increased.
In the observation period of 2003–2005 the number
of fish with affected kidneys decreased and affected
liver increased in Vaggatem, and similar trends were
seen in Skrukkebukta. The rate of affected gills and
gonads stayed lower. The frequency of malformations
in whitefish in Vaggatem and Skrukkebukta, as in Lake
Kuetsjarvi, remains almost the same in 2008 as in
the 90s, when intensity of stress on the waterways
was at the peak level.
Rajakoski
Similar fish malformations are identified in Rajakoski,
farthest from the industrial pollution source. The
transformation intensity in whitefish organs and tissues
was considerably less, and generally the liver
and kidney malformations in whitefish are incipient in
nature. However, the number of fish with such transformations
has not decreased during the decade of
observations (Figure 13).
Malformations in fish and
anthropogenic stress
Nickel and copper are predominant contaminants of
the area and the cause of high malformation rate of
fish. Accumulation of nickel in organs is in direct correlation
to proximity of a water body to the Pecheng-
Figure 11. Extreme degree of malformations of whitefish organs in Lake Kuetsjarvi.
%
100
80
60
40
20
0
1992 2004 2007 2009 2012 2013
changes of gonads stones in the kidney
changes of kidneys changes of liver
changes of gills
external changes
frequency occurance, %
100
80
60
40
20
0
2002
2004 2012
changes of gonads changes of liver
changes of kidneys changes of gills
Figure 12. Long-term malformations of whitefish organs in
Lake Kuetsjarvi.
Figure 13. Long-term malformations of whitefish organs in
Rajakoski.
86

anikel (Figure 14). Nickel and copper accumulation in
the bottom sediments of the lakes correlates strongly
(r=0.85–0.90) with the malformation frequency in
whitefish liver and kidneys (Figure 15). Whitefish is a
bottom feeder and this foraging strategy contributes
to high rates of heavy metal accumulation which may
have an effect on these organs. The most commonly
encountered change is the enlargement of connective
tissues of kidneys, which may be indicative of the persisting
nickel load in the lakes.
No considerable improvement of condition of fish
can be expected under the existing levels of the
anthropogenic stress. Also the climate change processes
may cause community-level transformations
in some water bodies, which may result in a decrease
in valued commercial species. This, in turn, leads to
decrease of the resource potential of the ecosystem
as such.
Discussion
The analysis of long-term observations of fish community
of the Pasvik River basin revealed changes,
which are associated with natural variability of the basic
biological characteristics of fish and negative consequences
of anthropogenic processes. Dominating
fish community structure complexes shift from salmon-whitefish
family to perch, smelt and cyprinid families
due to changing climate and persisting anthropogenic
stress (Kashulin et al. 2012). Such processes
were identified specifically in Vaggatem, Skrukkebuk-
Ni concentrations (μg / g d.w.)
8
a) Liver
6
Ni concentrations (μg / g d.w.)
15
b) Gills
10
4
2
5
0
20
10
0
0
Ku Sk Va Ra Ku Sk Va Ra
Ni concentrations (μg / g d.w.)
30
c) Kidney
Ni concentrations (μg / g d.w.)
2,0
d) Muscle
0,0
Ku Sk Va Ra Ku Sk Va Ra
Lake locality
Lake locality
frequency of occurance
100
80
60
40
20
0
Kuetsjarvi
DR whitefish
1,5
1,0
0,5
SR whitefish
Ku - Kuetsjarvi, Sk - Skrukkebukta, Va - Vaggatem, Ra - Rajakoski
Skrukkebukta
Ni
changes in kidneys
Ni in sediments
Vaggatem
Ni in sediments
Rajakoski
3500
3000
2500
2000
1500
1000
500
0
frequency of occurance
80
Figure 14. Nickel accumulation
levels in organs
of SR & DR whitefish of
studied lakes.
Figure 15. Correlation of whitefish liver and kidney malformation occurrence and nickel and copper accumulation in sediment
beds (µg/g dryweight
) of the studied lakes.
60
40
20
0
Kuetsjarvi
Skrukkebukta
Cu
Cu in sediments
800
changes in liver
700
Cu in sediments
600
Vaggatem
Rajakoski
500
400
300
200
100
0
87

ta and Rajakosk. Increase in the share of perch and
pike was noted as well as increase in their size and
the weight. At the same time in polluted Lake Kuetsjarvi
there were no major changes in the fish community
structure with whitefish being the most stable
family, as it is able to maintain relatively high population
due to early maturity, changes in life cycle strategy
and polymorphism (Kashulin et al. 1999). DR and
SSR forms of whitefish of Lake Kuetsjarvi have the
lowest values of linear growth of all the studied reservoirs
of the Pasvik River system. In these forms
unique early maturing with a minimal size (at the age
of 1+ with length 6–9 cm, weight 10 g) was observed,
which is considered as one of adaptations to survive
in heavily polluted waters.
Decrease in age, size and maturity classes of fish
populations in contaminated waters may serve as a
water quality criterion. In the whitefish populations
missing age classes, large proportion of young fish,
maturation of extremely young fish and large percentage
of fish skipping the spawning season were noted.
Anthropogenic stress is directly linked to fish growth
processes. The average length and weight of whitefish,
pike and perch seem to increase in proportion to
increasing distance from the Pechenganikel.
Assessment of fish communities revealed that even
though anthropogenic stress is decreasing, the condition
of fish remains without considerable improvement
at the level of communities, populations and individual
organisms. The examined areas are located at different
distances from the Pechenganikel plant and they
are most representative in terms of long-term assessment
of status of fish communities.
Fish species sensitive to water level regulation
(minnow, Phoxinus phoxinus) were found only in
Skrukkebukta and Skogmo sites, indicating that water
level regulation may have effect on littoral fish communities
of the Pasvik River. Fish densities and species
richness varied between the sites indicating that
effect of water level regulation may differ in different
river sections depending on littoral morphological characteristics.
The Pasvik River basin monitoring programme
should further focus on more comprehensive research
on fish species composition and monitoring of condition
of whitefish, vendace, pike and perch populations.
Length, weight and age structure, growth, reproduction
and feeding characteristics of fish should be the
main fish population indicators. Whitefish should be
used to assess the level of malformations in fish at the
organism level under the anthropogenic stress: status
of the kidneys (excretory system) is a critical parameter.
Identification of relations between the stress intensity
and negative biological responses of fish requires
an integrated approach taking into account both pathology
components and levels of heavy metal accumulation
in fish organs and tissues and in sediments.
Nickel and copper should be the main heavy metals
used in determining the anthropogenic stress on the
ecosystems of the border area. Observations of the
status of the fish fauna of the lakes should be carried
out once every three years.
88
Getting electrofishing equipment ready
Photo: Jukka Ylikörkkö.

References
Amundsen P-A., Bøhn T., Vaga G.H. 2004: Gill raker morphology and feeding ecology of two sympatric whitefish (Coregonus
lavaretus) morphs. Annales Zoologici Fennici 41: 291–300.
Bryuzgin V.L. 1969: Methods of study of fish growth by scales, bones and otoliths. Naukova Dumka. Kiev. 188 p.
Chugunova N.I. 1959: Fish age and growth study guide. Publisher of USSR Academy of Science. 164 p. (in Russian)
Dgebuadze Y.Y. 2001: Ecological regularities of fish growth variability. Nauka. 276 p. (in Russian)
Fevolden S-E., Amundsen P-A. 2013: Ecological speciation in postglacial European whitefish: rapid adaptive radiations into
the littoral, pelagic, and profundal lake habitats. Ecology and Evolution 3(15): 4970–86.
Kashulin, N.N., Lukin, A.A. & Amundsen, P.-A. 1999. Fish of subarctic freshwater systems as bioindicators of industrial pollution.
Kola Science Centre, Russian Academy of Sciences. Monograph. 170 p. (In Russian with an English summary)
Mina М.V., Klevezal G.А. 1976: Growth of animals. Nauka. 291 p. (in Russian)
Pravdin, I.F. 1960: Methods o fish studies. Moscow 376 p. (in Russian)
Pravdin, I.F. 1966: Handbook on the techniques of fish studies. Pischevaya Promyshlennost’. Moscow. 376 p.
(in Russian)
Reshetnikov, Yu 1980: Ecology and systematics of coregonid fish. Nauka. Moscow. 300 p. (in Russian)
Schmalhausen, I.I. 1935, Definitions of the Basic Conceptions and the Technique of Growth Investigation, Growth of Animals.
Biomedgiz. Moscow. p. 8–60. (in Russian)
Siwertsson A., Knudsen R., Amundsen P.-A. 2008: Temporal stability in gill raker numbers of subarctic European whitefish
populations. Advances in Limnology 63: 229–240.
Electrofishing catch. Photo: Jukka Ylikörkkö.
89

8 Long-term effects of metal
contamination, water regulation, species
invasion and climate change on the fish
of the Pasvik River
PER-ARNE AMUNDSEN
Fish community composition
Altogether 15 different fish species have been recorded
in the Pasvik watercourse, which is mainly formed
of lakes and reservoirs due to hydropower stations.
The most important fish species in these lacustrine
systems include whitefish, vendace, perch, pike, burbot,
nine-spined stickleback, brown trout and grayling.
Vendace invaded the Pasvik watercourse around
1990, after being introduced to Lake Inarijärvi in the
1960’s (Amundsen et al. 1999, 2012; Præbel et al.
2013) and has now become the dominant pelagic
species in the watercourse (Bøhn et al. 2008; Sandlund
et al. 2013). Whitefish has been the most numerous
fish species, occupying all major lake habitats in
high numbers (Amundsen et al. 1999). The whitefish
in the watercourse is polymorphic, consisting of three
different morphs, differentiated in particular by their
morphology and number of gill rakers, and referred
to as small sparsely-rakered (SSR), large sparselyrakered
(LSR) and densely-rakered (DR) whitefish
(Siwertsson et al. 2010). The three morphs have large
ecological differences; the LSR morph predominantly
residing in the littoral zone feeding on littoral zoobenthos,
the DR morph in the pelagic habitat feeding
on zooplankton, and the SSR morph in the profundal
utilizing typical profundal prey (Kahilainen et al. 2011).
Perch is also numerous in the watercourse, mainly
residing in the littoral zone and to some extent also
utilizing the profundal. The diet of perch includes several
ontogenetic niche shifts (Amundsen et al. 2003).
Pike is typically found in the shallow littoral, but has
in the latest years also more frequently been caught
in the pelagic habitat. Also pike goes through ontogenetic
niche shifts. Burbot is a benthic dwelling fish
species, the abundance of which is low relative to e.g.
perch and pike. Nine-spined stickleback has a key
role in the food web of the lacustrine ecosystems in
the watercourse, being a dominant prey for the small
to intermediate sized predatory fishes, in particular
perch and burbot (Figure 1; Amundsen et al. 2003).
Brown trout
Pike
Perch
Burbot
Figure 1. Summary of the basic
food web structure of the lacustrine
fish communities in the Pasvik watercourse
(line thickness indicates
the importance of the different links.
Stippled lines represent unconfirmed
links).
Vendace
Pelagic
DR whitefish
9-sp. st.b.
Benthic
LSR whitefish
90

The brown trout in the Pasvik watercourse is a fastgrowing,
typically piscivorous form, which mainly feed
on coregonid prey (vendace and DR whitefish) in the
pelagic (Jensen et al. 2004, 2008). The water level regulations
in the Pasvik watercourse have chiefly reduced
the spawning and nursery areas for brown trout,
and annual stocking of brown trout is carried out to
compensate for the reduced reproduction and recruitment
possibilities. Also grayling has suffered from the
hydropower stations due to the loss of stretches with
running water.
Food web structure and
stable isotopes
The food web of the lake ecosystems in the watercourse
consist of two main compartments originating
from the pelagic and benthic primary production,
respectively (Figure 1). In the native ecological community,
the two most common whitefish morphs, DR
and LSR whitefish, have central roles in each of these
compartments; the DR morph utilizing the zooplankton
resources and the LSR morph the benthic
invertebrates mainly in the littoral zone (additionally
the less abundant SSR morph is utilizing the benthic
invertebrates in the profundal). Vendace has now become
the key zooplankton predator and the dominant
fish species in the pelagic habitat. Brown trout is the
key top predator in the pelagic compartment, feeding
predominantly on vendace and DR whitefish. In the
benthic part of the trophic network, adult perch, burbot
and pike are piscivorous species utilizing in particular
nine-spined stickleback and whitefish as prey. Pike
constitutes the apex predator of the whole aquatic
network in the Pasvik watercourse (Figure 1). Pike,
and more recently also perch and burbot, have started
to include vendace in their diet, and the separation of
the pelagic and benthic food-web compartments has
therefore become less pronounced after the vendace
invasion in the watercourse.
Stable isotope analysis provides long-term integrated
information about the main nutritional sources,
thereby constituting a cost-effective tool to explore
the trophic ecology of freshwater organisms and the
food-web structure of aquatic ecosystems (Boecklen
et al. 2011, Layman et al. 2012). The carbon and nitrogen
stable isotope ratios (expressed as δ 13 C and
δ 15 N, respectively) can distinguish between resources
from the three principal lake habitats; littoral, pelagic
and profundal (Vander Zanden & Rasmussen 1999,
Syväranta et al. 2006).
A summary plot of the mean carbon and nitrogen
stable isotope ratios for key taxa in the Pasvik lakes
food webs reveals a distinct pattern of trophic levels
and resource utilization (Figure 2), chiefly reflecting
the trophic network established from habitat use and
stomach contents data (Figure 1). Along the δ 15 N-
axis, the invertebrates are positioned at lower values,
which reflect their low positions in the trophic network.
The profundal invertebrates (chironomids) show elevated
δ 15 N-levels typical for them as compared to the
δ 15 N
12
Brown trout
Pike
10
SSR whitefish
Perch
8
Vendace
9-sp. stickleback
DR whitefish
LSR whitefish
6
Chironomids ( profundal )
4
Zooplankton
Chironomids (littoral)
2
0
Snails
-35 -30 -25 -20 -15
δ 13 C
Figure 2. Mean stable isotopes
ratios (δ 13 C and δ 15 N) of important
taxa in the lacustrine food
webs of the Pasvik watercourse
(data mainly from Vaggatem).
91

littoral and pelagic invertebrates, a pattern that is also
reflected in the relatively high δ 15 N-levels of the profundal
dwelling, invertebrate-feeding SSR whitefish.
Invertebrate-feeding fish species from the pelagic
and littoral habitats dominate the intermediate δ 15 N-
levels whereas piscivorous species are positioned at
the highest trophic levels as indicated by their high
δ 15 N-values (Figure 2). Perch is positioned intermediate
to the coregonids and pike, reflecting their more
omnivorous diet consisting both of invertebrate and
fish prey. Along the δ 13 C-axis the profundal and pelagic
invertebrates have lower values than the littoral
invertebrates. Similarly the coregonid species utilizing
pelagic and profundal resources are positioned towards
lower δ 13 C-values than the predominantly littoral
dwelling LSR whitefish. Among the top predators,
brown trout, which typically feed in the pelagic habitat,
had somewhat lower δ 13 C-values than pike (Figure 2).
Anthropogenic impacts
The Pasvik watercourse suffers from a multitude of
stressors that encompass chemical, physical and biological
factors, in particular represented by large pollution
outputs from the Pechenganikel extensive water
level regulations, introduction and invasion of non-native
species, compensatory fish stockings, and unregulated
and unrecorded fish exploitation. Over the latest
decades, global warming scenario has become
an additional stressor. In relation to these extant threats
and in order to enhance the general knowledge
of subarctic freshwater ecosystems in this region, the
fish communities of lakes in the Pasvik watercourse
have been subject to extensive long-term biological
studies with annual sampling since 1991.
Heavy metal contaminations in fish
Several heavy metals have been analyzed in different
tissues from six fish species and at three different sites,
covering four sampling periods (Period 1: 1991–
1992, Period 2: 2002–2005, Period 3: 2007–2008, Period
4: 2012–2013). The studied lakes are Kuetsjarvi,
Skrukkebukta and Vaggatem. Some additional samples
are also available from Rajakoski (Period 2 and
4) and Lake Inarijärvi (Period 2). Studied fish species
are DR and LSR whitefish, perch, pike, brown trout
and vendace, and tissue samples have been retrieved
from muscle, liver, gills, kidney and skeleton. Elements
examined in all sampling periods include nickel
(Ni), copper (Cu), cadmium (Cd), chromium (Cr), zinc
(Zn) and mercury (Hg).
A temporal analysis of the Ni contents in different
tissues of the DR and LSR whitefish morphs revealed
no distinct variations throughout the four time periods
from 1991 to 2013. A profound and significant decline
in contamination levels with increasing distance from
the smelters (i.e. from Kuetsjarvi to Skrukkebukta to
Vaggatem) was, however, evident for all the examined
time periods. This could also be seen for the Ni
contents in different tissues of perch and pike. Similar
patterns were also revealed for Cu and Cd. Also for
the tissue contents of Cr and Zn there were no distinct
variations through time. Hence, no major changes
in the contamination levels of these elements in fish
tissue appear to have occurred over the time period
of 1991–2013.
In contrast, a distinct temporal pattern was evident
for the mercury (Hg) contents in fish tissues. Both in
Kuetsjarvi (Figure 3a) and Skrukkebukta & Vaggatem
(Figure 3b) there was a significant increase in the Hg
contents in muscle tissue of most fish species over
the four sampling periods from 1991 to 2013. The inc-
Hg contents in muscle tissue
( μg g - 1 fish dry weight)
2,0
a) Kuetsjarvi
2,0
b) Vaggatem & Skrukkebukta
Hg contents in muscle tissue
( μg g - 1 fish dry weight)
1,5
1,5
1,0
1,0
Figure 3. Temporal changes in Hg levels in muscle
tissue of fish from a) Kuetsjarvi and b) Skrukkebukta
& Vaggatem (samples from these two lakes were
combined to strengthen the observation numbers as
no large differences in Hg levels were evident between
these two localities). Time periods 1: 1991–
1992, 2: 2002–2005, 3: 2007–2008, 4: 2012–2013
0,5
0,0
1 2 3 4
Time period
DR whitefish SR whitefish
0,5
0,0
Vendace
1 2 3 4
Time period
Perch Pike Brown trout
92

ease in Hg contamination over the study period was
particularly large for the predatory species and was
most distinct in Skrukkebukta & Vaggatem. The generally
higher contamination levels in predatory species
than in the coregonids are to be expected as Hg is an
element that accumulates in organisms and thus typically
increases with increasing trophic levels within
the food webs. Similarly, the Hg contents in fish also
tend to increase with increasing fish size, which could
be seen for brown trout, perch, pike, and LSR whitefish
but was not evident for DR whitefish and vendace
which have more narrow size ranges.
The observed increase in the Hg levels of the predatory
fishes may be related to the recent ecological
changes following the vendace invasion in the watercourse,
as pelagic pathways are known to be of large
importance in bioaccumulation and magnification
of Hg in subarctic lake food webs. Another plausible
explanation for the high levels of Hg and the distinct
increase in Hg levels in fish over the latest decade
is related to the ongoing global climate changes.
Increased temperatures and a higher run-off due to
increased precipitation have already been demonstrated
for the watercourse (Chapter 1). This has likely
resulted in a more extensive wash-out of pollutants
from the large catchment area and into the watercourse,
where biomagnification and accumulation of Hg
through the food web rapidly may result in elevated
contamination levels in the top predators (conf. e.g.
Harris et al. 2007).
Water regulations
Following the establishment of seven hydropower stations
along the Pasvik watercourse there have been
large changes in the physical characteristics: large
areas were flooded, previous rapids and waterfalls
disappeared, and the former river system is now dominated
by consecutive lakes and reservoirs. Principal
spawning, nursery and feeding areas for brown
trout and grayling were severely degraded and reduced
due to the disappearance of the riverine stretches
of the watercourse, resulting in strong declines
in the abundance of these species (Kristoffersen
1984, Arnesen 1987). The large physical changes of
the watercourse have benefitted typical lake-dwelling
fish species like whitefish, perch and pike, especially
through the development of large reservoirs. The
dam constructions have resulted in a fragmentation of
the watercourse, making upstream fish migration impossible
and downstream migration infeasible. For the
fish populations the fragmentation and migration limitations
may have resulted in some genetic constraints
for some of the fish populations, but this has yet not
been explored.
Brown trout stocking and vendace invasion
The Pasvik brown trout recruitment potential suffers
from the hydropower plants of the Pasvik River and
due to this the local Norwegian hydropower company
is carrying out an imposed annual stocking of
5000 large-sized trout. The stockings are important
for the present brown trout population as stocked fish
comprise >80 % of the total catches. From 1998 to
2008, the contribution of stocked fish in the catches
has distinctly decreased, which suggests that the natural
production of brown trout may have slightly improved,
possibly as a result of a larger spawning population
due to the contribution of stocked fish. Vendace
has been the dominant prey over this time period, with
an increasing dietary contribution from around 75 %
in 1998 to nearly 100 % in 2008. Hence, the vendace
invasion and the establishment of a high population
abundance of the invader have provided a new prey
source for the piscivorous trout, and may thus have
increased the production potential of brown trout in
the watercourse.
The strategy for the stocking programme appears
good, especially in respect to the exclusive use of
brood fish from the local trout population. However,
artificial selection may occur during the collection of
the brood stock and subsequently in the breeding and
rearing facilities, potentially leading to a reduced or
altered genetic diversity. Further studies of the brown
trout population should therefore be implemented,
including a genetic survey. Moreover, the remaining
spawning and nursery areas for brown trout in tributary
streams need particular attention and protection,
especially since the trout has an obligate role in the
lifecycle of the red-listed freshwater pearl mussel Margaritifera
margaritifera (e.g. Aspholm 2013).
The vendace invasion started from Lake Inarijärvi,
where the species was introduced in the headwaters
on two occasions in the 1950s and -60s (Mutenia &
Salonen 1992), and by 1995 it was apparently present
along the whole watercourse (Amundsen et al.
1999). Within few years the invader became an important
pelagic fish species in lakes in the Pasvik watercourse
(Amundsen et al. 1999, Bøhn et al. 2004,
2008). However, the vendace population also entered
a typical fluctuating ‘boom-and-bust’ development
with large variations in population density (Salonen et
al. 2007, Sandlund et al. 2013), resulting in a highly
93

variable and unpredictable ecological situation in the
watercourse.
In Vaggatem the vendace population rapidly increased
in abundance after its arrival in 1991 and had
by 1998 attained a peak density in the pelagic habitat.
However, over the next couple of years there was an
abrupt decline in the vendace density, which stayed
low until 2003. Later, the vendace density has shown
large fluctuations with mainly three years intervals
between peaks.
The invasion and rapid population increase of vendace
in Vaggatem had an immediate and dramatic effect
on the DR whitefish population. Over the first 3-4
years the total density of DR whitefish remained at a
constant level. However, only one year after the arrival
of vendace a major behavioral shift occurred, where
most of the DR whitefish population disappeared
from the pelagic, and was relegated into the littoral
and profundal habitats. This situation was maintained
for a few years, but after 1995 a dramatic decline occurred
in the DR whitefish abundance. Even during
the period from 2000 to 2003 when the vendace density
seemingly was at low levels, no apparent rebuild
of the DR whitefish population occurred.
Vendace can utilize zooplankton more efficiently
than DR whitefish (Bøhn & Amundsen 2001) which
can be seen from the long-term changes in zooplankton
density, which decreased following the vendace
invasion. The zooplankton resource appears to be
inaccessible as a significant dietary source for DR
whitefish when vendace is present in high densities.
Assumedly the differences in vendace populations
between localities (Figure 4) are related to a variable
resistance towards the invading species between sites
and among fish communities of different structure
and dynamics. In particular, the DR whitefish populations
in Skrukkebukta and Kuetsjarvi consist of smaller-sized
individuals and have a shorter generation
time than in Vaggatem, which may make them more
capable of responding to the competitive impacts of
the invading vendace. In Kuetsjarvi, local adaptations
of the whitefish populations to the heavy pollution may
also have made it more difficult for the invading vendace
to get an upper hand in competition and to establish
in high densities. Furthermore, at the time of
the invasion the propagule pressure was likely much
larger in the upper parts of the watercourse, which
may have led to an abrupt shift in competitive and numerical
dominance in favor of the vendace.
Vendace and whitefish belong to the same genus
(Coregonus) and hybridization between the two species
is plausible even though it has not been documented
in the Pasvik watercourse. In contrast, a breakdown
of the reproductive isolation between the LSR
and DR whitefish morphs is documented to have occurred
following the invasion of vendace (Bhat et al.
2014). It was revealed that the frequency of hybrids
had increased from 34 % in 1993 to nearly 100 % by
2008. The extensive hybridization between this morph-pair
appears to reflect a situation of “speciation in
reverse”, where the vendace invasion has reversed
the incipient speciation process that has led to the formation
of the whitefish morphs, collapsing these into
a hybrid swarm and creating a situation which potentially
may have large consequences for the biodiversity
and functioning of these ecosystems.
Climate change impacts
Three key objectives have been addressed: 1. Are
there any significant changes to be seen in the temperature
regime of the watercourse; 2. Are there any
evidence of temperature effects on fish growth (juve-
% vendace in pelagic catches
100
80
Vaggatem
60
40
Skrukke -
bukta
Figure 4. The contribution of vendace
in pelagic fish catches in Vaggatem,
Skrukkebukta and Kuetsjarvi in the period
from 1991 to 2013.
20
Kuetsjarvi
0
-90 -92 -94 -96 -98 -00 -02 -04 -06 -08 -10 -12 -14
Year (19 -/20 -)
94

nile coregonids); 3. Are there any changes in the fish
community composition that can be related to temperature-induced
changes in species interactions?
The water temperatures in the Pasvik watercourse
have been monitored on a daily basis since 1991,
and the water temperatures between 1975 and 1991
have furthermore been estimated from a modeling effort
based on measured air temperatures (Gjelland et
al. 2013). We found that average summer water temperature
(i.e. July–September) increased significantly
from 1975 to 2013 with on average 0.05 °C/year. Over
the 38 years, the average summer water temperature
increased from 11.89 °C to 13.84 °C, i.e. an increase
of 1.95 °C for the total period, equivalent to a mean
temperature increase of 0.51 °C per decade.
According to modelled temperature, precipitation
and runoff predictions for the Pasvik watercourse, the
water temperature and length of the ice-free season
will continue to increase throughout the 21 century
(Gjelland et al. 2013), as will also the annual precipitation
and runoff. This sets the scene for potential
large ecological changes in the watercourse.
Changes in summer water temperatures may affect
the juvenile growth of the coregonids in the Pasvik
watercourse. It is suggested that coregonid larval
growth is primarily controlled by temperature and
thereafter by food availability. Hence, a slight shift in
temperature regime may potentially have a dramatic
effect on growth of larval and juvenile coregonids
(Eckmann and Rösch 1998; Perrier et al. 2012). To
explore this, the back-calculated length at age 1+ (and
thus the growth performance during the first year of
life) of vendace and DR and LSR whitefish from Vaggatem
and Skrukkebukta were matched to the corresponding
summer water temperatures to explore
possible climate and temperature effects on juvenile
coregonid growth over an approx. 20 year time period.
The study revealed that an increase in temperature
evidently will increase larval and juvenile growth. The
present temperature regime in the Pasvik watercourse
may be favorable for the juvenile growth rate of the
LSR whitefish morph but the expected future increase
in temperatures will likely shift the favorability towards
vendace. Hence, as vendace and DR whitefish compete
strongly for the pelagic resources and since a
further increase in water temperatures likely will be
favorable for the vendace, the outcome may be an
even stronger negative effect on the DR whitefish population.
Climate warming is expected to induce complex
changes in fish community structure (Jeppesen et al.
2010). Whitefish is a cold-water stenothermic species
with optimum growth at 18 o C (Siikavuopio et
al. 2013), whereas perch is a cool-water eurythermic
species with optimum growth at 23 o C (Fiogbe & Kestemont
2003). Hence, perch is considered as the species
that most likely will benefit from increasing temperatures
at the expense of e.g. whitefish (Graham &
Harrod 2009, Hayden et al. 2014).
Our long-term data set has been analyzed in respect
to the contribution of perch in the littoral habitat
(Figure 5). In Vaggatem, a significant increase occurred
over the first part of the study period from a contribution
of ca. 20 % in 1991 to 50–60 % around 2003.
In Skrukkebukta, the development pattern of the littoral
fish community was very clear-cut with a distinct
and significant increase in the perch contribution from
1993 to 2013, from around 5 % in the start to nearly
50 % at the end of the study period. These findings
% perch in littoral catches
90
80
70
60
Vaggatem
50
Skrukke -
40
bukta
30
20
10
0
-90 -92 -94 -96 -98 -00 -02 -04 -06 -08 -10 -12 -14
Year (19-/20-)
Figure 5. The contribution of perch (with
fitted trend lines) in littoral samples
from Vaggatem and Skrukkebukta in
the time period from 1991 to 2013.
95

strongly suggest that perch have benefitted from the
increasing temperatures.
The present findings demonstrate that climate
change impacts already are in effect in the Pasvik watercourse,
having induced a significant increase in the
mean summer water temperatures over the last decades
and seemingly also induced important ecological
responses and effects. In particular, it has been demonstrated
that the juvenile growth of the coregonids
is significantly affected by the increased water temperatures,
which potentially may affect their interspecific
interactions. Furthermore, a change in the fish community
composition of the littoral zone is also evident,
with an increase in the contribution of perch. Additionally,
the increase in the levels of Hg in fish over the
latest years may suggestively be a climate change
consequence related to increased precipitation and
runoff. The multitude of stressors affecting the Pasvik
watercourse may enhance potential climate change
impacts in the watercourse, making the ecosystems
less resistant and thus more vulnerable to the induced
changes.
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Photo: Juha Riihimäki
Taking the water temperature at fishing
site. Photo: Jukka Ylikörkkö.
97

9 Contaminants in fish of the Pasvik River
GUTTORM N. CHRISTENSEN, HELÉN JOHANNE ANDERSEN, GEIR DAHL-HANSEN, NIKOLAY KASHULIN, PETR
TERENTJEV, DMITRII DENISOV
The main contaminant sources to aquatic and terrestrial
environments in the area are the Pechenganikel
mining and metallurgical company’s smelter in Nikel
and roasting plant in Zapolyarny. In addition to this,
the area is also exposed to long-range airborne pollutants.
The area has been identified by the Arctic Monitoring
and Assessment Programme (AMAP 2000) as
a “Key monitoring area,” where pollution, emissions
and their effects are to be monitored.
Studies carried out in the early 1990s revealed numerous
acidified and heavy metal polluted lakes in
the border area (Traaen et al. 1991, 1992, Moiseenko
1994, Dauvalter and Rognerud 2001). Water quality
monitoring has shown that the heavy metal concentrations
in the lakes in this area have been increasing
since 2004. Heavy metal levels in fish are well studied
by Russian and Norwegian scientists (Amundsen et
al. 1993, 1997, Arnesen et al. 1996, Kashulina & Kashulin
1997, Moiseenko et al. 1995, Lukin et al. 2003).
Previous screening of contaminants demonstrated
elevated levels of persistent organic pollutants
(POPs) in different fish species in the Pasvik River
(Stebel et al. 2007, Christensen et al. 2007, Christensen
2008). In previous screening studies, the highest
levels of POPs were found in fish from Lake Kuetsjarvi
and it was a tendency of decreasing levels of POPs
with increasing distance to the smelter.
The aims of this study were to follow up the findings
of the previous project, “State of the environment in
the Norwegian, Finnish and Russian border area” that
was carried out during 2003–2006 (Stebel et al. 2007).
Based on the previous studies the main contaminants
in fish are mercury, polychlorinated biphenyls(PCBs),
pesticides and polybrominated diphenyl ethers (PB-
DEs). Fish from the Pasvik River is an important food
source for the people in the area. There are more than
15 species of freshwater fish in the Lake Inarijärvi and
the Pasvik River watercourse. The fish community is
a mixture of eastern, western and introduced species.
The most important fish species for food consumption
are whitefish (Coregonus lavaretus), trout (Salmo
trutta), perch (Perca fluviatilis) and pike (Esox lucius).
The results from this study can be used to evaluate
the levels of contaminants related to food safety.
The most distinct contaminants in the area
Mercury (Hg) occurs naturally in the environment and
has been used in numerous medicinal, commercial
and industrial applications. Today mercury presents
risks to Arctic wildlife and human populations (Arctic
Monitoring and Assessment Programme, AMAP
2011). Mercury is also one of the prioritized substances
under The EU Water Framework Directive (WFD)
and it is included on Norway’s priority list of hazardous
substances. It is of particular concern that mercury
levels are continuing to rise in parts of the Arctic, despite
reductions in anthropogenic emissions. There are
several reasons for this: increased emissions in Asia,
increased riverine discharge, thawing of permafrost
and local-scale climate change (AMAP 2011).
Mercury enters the Arctic environment via long-range
transport from human sources at lower latitudes.
The European limits for allowable levels of mercury
in fish are 0.5 mg/kg ww. The documented elevated
levels of mercury and especially the increasing trends
of mercury in the environment are of a great concern
for the Arctic countries (AMAP 2011).
Persistent organic pollutants (POPs) are a group
of chemicals which persist in the environment, may
bioaccumulate in human and animal tissues and are
toxic. They also have the potential to be transported
long distances and be deposited far away from their
place of release.
Polychlorinated biphenyls (PCBs) were first manufactured
in 1929 and produced in many countries.
PCBs have been used extensively in a variety of industrial
products and as plasticisers. In Russia, production
of PCBs was terminated between 1987 and
1993 and they are listed as carcinogenic. The International
Agency for Research on Cancer ranks PCBs as
a probable human carcinogen. Chronic low-level exposure
to PCBs can cause liver damage, reproductive
abnormalities, immune suppression, neurological and
endocrine system disorders, delayed infant development
and stunted intellectual function.
Dichlorodiphenyltrichloroethane (DDT) was widely
used as a pesticide and insecticide, but has since the
early 1970s been banned in North America, Europe
and the former USSR. However, it continues to be us-
98

ed in Asia, Africa, Central and South America (Voldner
and Li 1995), resulting in a continued global source.
Total DDT is the sum of the DDT structural analogs
and breakdown products. DDT has been shown to
be a hormone-disrupting chemical that can affect the
reproductive and nervous systems.
Hexachlorobenzene (HCB) has been used as a
fungicide, solvent and as a manufacturing intermediate
or additive. Production and use has ceased in
many countries. HCB continues to be created as a
by-product in the manufacture of many chlorinated
solvents and pesticides and in other chlorinated processes.
It is also released in the burning of municipal
waste, during incomplete combustion. In Russia, HCB
was used until 1990, and was banned in 1997. HCB is
toxic and can damage the liver, thyroid and kidneys,
as well as the endocrine, immune, reproductive and
nervous systems.
Chlordane is a versatile, broad-spectrum contact
insecticide used mainly for non-agricultural purposes.
Recently, the use of chlordane has been restricted in
many countries, due to its toxic effects and capacity to
persist and bioaccumulate. It is banned in Russia (de
March et al. 1989). Chlordane exposure has been linked
to liver and blood disorders, severe neurological
effects and damage to the endocrine and reproductive
systems.
Different mixtures of polybrominated diphenyl ethers
(PBDEs) are used as additive flame retardants
in plastics and textiles. (Bergman 1989). There is evidence
that some PBDEs bioaccumulate and cause
toxic effects at low levels. The United States Environmental
Protection Agency (EPA) has classified decabromodiphenyl
ether as a possible human carcinogen.
PBDEs are also endocrine disrupters.
Study area and sampling locations
The Pasvik River catchment area is the main freshwater
system in the region, covering an area of approximately
1250 km 2 . It has a catchment area of 18 404
km 2, of which approximately 70 % belongs to Finland,
25 % to Russia and 5 % to Norway. The watercourse
constitutes a subarctic system with high biodiversity
and production of fish and other aquatic organisms.
The fish populations are important food resources for
the locals and there is a long tradition to utilize the
resources both for commercial and recreational fishing
(Aspholm 2004, Aspholm 1996). Detailed descriptions
of the study area are given in numerous report
and publications (e.g. Arnesen et al. 1996, Moiseenko
et al. 1995, Amundsen et al. 1993, 1997, Lukin et al.
2003, Stebel et al. 2007).
Sampling of fish was carried out in in September
2012 and September 2013. The following lakes in the
Pasvik River were sampled: Vaggatem (Tjerebukta
and Ruskebukta), Lake Kuetsjarvi and Skrukkebukta
(Table 1, Chapter 3, Introduction, Figure 1).
Sampling procedure
Fish sampling was performed in the littoral (< 8 m),
profundal (> 10 m) and pelagic habitats (0–6 m) using
gillnets. In all studied lakes, the whitefish is represented
by two different morphs, differentiated by their
number and morphology of gill rakers and referred to
as sparsely-rakered (SR) and densely-rakered (DR)
whitefish (Amundsen et al. 2004). The two whitefish
morphs exhibit distinct genetic and ecological differences
(Amundsen 1988, Amundsen et al. 2004), and
are treated as functional species in the analysis and
presentation of the results. Fish were identified to the
species level.
The following fish species were collected for contaminant
analyses during 2012–2013: pike, perch, whitefish
and trout.
Each fish was measured for fork length and weight,
sex and stage of maturation were recorded. Otoliths
were sampled from whitefish and opercula from perch
for age determinations. The tissue samples (muscle
and liver, weight 5–20 g) were either packed in preburned
aluminium foil for POPs samples or plastic
zip-lock bags for metal analysis. Samples were stored
frozen (-20 °C) in the field and transported frozen to
the laboratory for analyses.
Materials and methods
Analyses
Muscle tissue from pike, perch, SR whitefish, DR whitefish
and trout were selected for POPs (55 fish) and
heavy metal analyses (97 fish) (Table 2).
Persistent organic pollutants analyses carried out
in this project were chlorinated pesticides, polychlorinated
biphenyls (PCBs), planar and non-orthosubstituted
congeners of PCBs and brominated flame retardants
(PBDEs).
Analysed heavy metals were mercury (Hg), arsenic
(As), cadmium (Cd), lead (Pb), copper (Cu), cobolt
(Co), zinc (Zn), lithium (Li), nickel (Ni), iron (Fe) and
magnesium (Mn). Also lipids were analyzed.
Analyses were carried out at Typhoon analytical laboratory
(Obninsk, Russia). Detailed descriptions of
99

analytical methods used for determination of environmental
contaminants, along with information on QC/
QA, are given in Christensen et al. 2015.
The following persistent pollutants were determined
in the biological samples:
• chlorinated pesticides and industrial organochlorines:
DDT-group, HCH, hexachlorobenzene (HCB),
chlordanes, mirex, endrin and dieldrin.
• ortho-substituted congeners of polychlorinated
biphenyls.
• planar and non-ortho-substituted congeners of
PCBs
• brominated flame-retardants
Results and discussion
The analysed material was a selection of the collected
material from 2012 and 2013. The aim was to analyse
the main species from the three sites, Vaggatem
(Ruskebukta and Tjerebukta), Lake Kuetsjarvi and
Skrukkebukta. The main fish species in all the localities
analysed were pike, perch and large sparsely-rakered
whitefish (LSR whitefish). In addition, also densely-rakered
whitefish (DS whitefish) from Vaggatem
and brown trout from Skrukkebukta were analysed.
There were some differences in the fish material regarding
size of the different species from the different
sites (Table 3). In general, pike, perch and LSR whitefish
were larger in lake Vaggatem than in lakes Kuetsjarvi
and Skrukkebukta. In average the pike, perch
and LSR whitefish were smallest in Lake Kuetsjarvi.
For detailed information about this study, see Christensen
et al. 2015.
Table 1. Study localities in the Pasvik River.
Lake Country Approx. distance
from the smelters
Fish species
Vaggatem Norway 40 km, upstream pike, perch, SR whitefish, DR whitefish
Kuetsjarvi Russia 5 km, downstream pike, perch, SR whitefish
Skrukkebukta Norway 5 km, downstream pike,perch, SR whitefish, trout
Table 2. Analysed fish material from lakes Vaggatem, Kuetsjarvi and Skrukkebukta.
Lake Species Number of analysed POPs Number of analysed metals
Vaggatem pike 5 13
perch 5 10
SR whitefish 5 10
DR whitefish 5 10
Kuetsjarvi pike 5 5
perch 5 9
SR whitefish 5 10
Skrukkebukta pike 5 5
perch 5 10
SR whitefish 5 10
trout 5 5
100

Table 3. Summary of weight and length (max, min and std) and average concentrations of ΣPCB, ΣDDT, PBDE, Hg, Ni and Cu of
the analysed fish material from Vaggatem, Kuetsjarvi and Skrukkebukta.
Component
Pike
Vaggatem (n = 10) Kuetsjarvi (n = 5) Skrukkebukta (n = 5)
Average Max Min Std Average Max Min Std Average Max Min Std
weight (g) 1907 3440 823 738 590 1215 256 372 1335 3697 568 1337
length (cm) 66.7 81.4 49.9 8.79 39.8 50.8 33.0 6.61 51.2 73.5 40.9 13.5
Σ PCB (ng/g ww) 2.57 4.47 1.33 1.17 6.15 7.85 5.18 1.03 4.95 9.73 3.28 2.71
Σ DDT (ng/g ww) 0.304 0.460 0.190 0.112 1.26 1.61 0.960 0.255 0.700 1.58 0.260 0.522
PBDE (ng/kg ww) 25.6 43.4 9.33 14.9 41.0 50.6 29.1 8.51 66.2 120.2 35.4 33.5
Hg (mg/kg ww) 0.274 0.486 0.096 0.105 0.078 0.119 0.051 0.026 0.312 0.851 0.118 0.306
Ni (mg/kg ww) 0.211 0.290 0.180 0.033 0.320 0.410 0.230 0.065 0.372 0.710 0.180 0.205
Cu (mg/kg ww) 0.202 0.250 0.170 0.025 0.312 0.470 0.190 0.118 0.242 0.280 0.160 0.048
Component
PERCH
Vaggatem (n = 13) Kuetsjarvi (n = 9) Skrukkebukta (n = 10)
Average Max Min Std Average Max Min Std Average Max Min Std
Weight (g) 311 409 251 49.6 192 440 104 109 251 425 144 113
Length (mm) 28.2 31.0 26.5 1.47 22.4 29.0 19.0 3.34 25.9 32.2 22.0 3.70
Σ PCB (ng/g ww) 0.342 0.680 0.020 0.304 3.95 7.65 0.140 3.50 7.48 13.1 4.97 3.36
Σ DDT (ng/g ww) 0.198 0.280 0.160 0.056 0.503 0.950 0.060 0.374 1.35 2.95 0.850 0.904
PBDE (ng/kg ww) 1.44 2.67 1.00 0.722 16.5 25.0 2.75 11.0 154 482 47.3 184
Hg (mg/kg ww) 0.144 0.21 0.102 0.033 0.068 0.126 0.038 0.027 0.438 1.49 0.076 0.436
Ni (mg/kg ww) 0.229 0.270 0.210 0.022 0.283 0.340 0.220 0.039 0.470 0.670 0.370 0.083
Cu (mg/kg ww) 0.193 0.270 0.130 0.038 0.214 0.260 0.160 0.032 0.151 0.390 0.110 0.085
Component
LSR WHITEFISH
Vaggatem (n = 10) Kuetsjarvi (n = 10) Skrukkebukta (n = 10)
Average Max Min Std Average Max Min Std Average Max Min Std
Weight (g) 1109 1937 523 420 323 981 156 246 381 767 217 171
Length (mm) 43.0 49.8 35.3 4.81 28.2 38.5 24.2 4.38 32.0 40.3 26.7 4.05
Σ PCB (ng/g ww) 0.052 0.140 0.020 0.052 6.69 10.2 4.93 2.39 5.75 13.06 1.94 4.60
Σ DDT (ng/g ww) 0.220 0.330 0.160 0.070 1.69 2.30 0.940 0.6157 1.30 3.38 0.430 1.21
PBDE (ng/kg ww) 1.43 2.80 1.00 0.781 44.2 60.4 25.0 15.2 51.1 83.5 26.9 25.3
Hg (mg/kg ww) 0.036 0.051 0.022 0.009 0.018 0.033 0.011 0.007 0.041 0.055 0.024 0.010
Ni (mg/kg ww) 0.241 0.290 0.190 0.031 0.254 0.330 0.210 0.039 0.297 0.360 0.250 0.033
Cu (mg/kg ww) 0.185 0.270 0.140 0.044 0.300 0.370 0.240 0.045 0.210 0.240 0.190 0.016
Component
DR WHITEFISH
Component
TROUT
Vaggatem (n = 10)
Average Max Min Std
Weight (g) 143.9 310.0 91.0 65.8
Length (mm) 23.1 29.6 20.3 2.8
Σ PCB (ng/g ww) 0.8 3.0 0.2 1.2
Σ DDT (ng/g ww) 0.10 0.21 0.03 0.09
PBDE (ng/kg ww) 2.58 5.49 1.00 2.07
Hg (mg/kg ww) 0.05 0.09 0.03 0.02
Ni (mg/kg ww) 0.251 0.31 0.17 0.043
Cu (mg/kg ww) 0.184 0.22 0.15 0.025
Skrukkebukta (n = 5)
Average Max Min Std
Weight (g) 1544 3657 435 1464
Length (mm) 46.5 67.2 34.0 14.7
Σ PCB (ng/g ww) 10.6 14.9 5.99 3.79
Σ DDT (ng/g ww) 1.99 3.12 0.81 1.079
PBDE (ng/kg ww) 149.3 270.0 1.00 108.9
Hg (mg/kg ww) 0.161 0.273 0.099 0.071
Ni (mg/kg ww) 0.552 0.870 0.430 0.180
Cu (mg/kg ww) 0.148 0.170 0.120 0.022
101

Mercury
The highest average concentrations of mercury were
measured in perch (0.44 mg/kg ww) and pike (0.31
mg/ww) from Skrukkebukta (Figure 1). The highest
concentration measured in a single perch from Skrukkebukta
was 1.49 mg/kg ww. The average levels of
mercury in perch from Skrukkebukta are close to the
European limits for allowable levels of mercury in fish
(0.5 mg/kg ww). The levels of mercury in Kuetsjarvi
were significantly lower in pike, perch and whitefish
compared to Vaggatem and Skrukkebukta. However,
the levels of mercury in sediments are considerably
higher in Lake Kuetsjarvi compared to other sites in
the Pasvik River. The reason for this is not clear but
is probably both related to smaller fish (Figure 1) in
Lake Kuetsjarvi and the fact that the levels of other
contaminants are so high that this limits the methylation
of mercury in the sediments. The levels of mercury
in whitefish are, as assumed, low, compared to the
predatory fish species (pike, perch and trout). The levels
of mercury in this study are comparable with the
results from 2008 (Christensen 2008) but the levels in
Skrukkebukta and Vaggatem are higher compared to
other lakes in Finnmark (Fjeld et al. 2010, Christensen
et al. 2008).
Copper and nickel
The highest average concentration of copper and
nickel in muscle tissue was measured in fish from Lake
Kuetsjarvi (Figure 2). There was no clear difference
between the levels in fish from Skrukkebukta and
Vaggatem. The nickel levels in almost all the samples
from this study are higher than in the samples from
2008 (Christensen 2008).
Polychlorinated biphenyls (PCBs)
The highest average concentrations of ∑PCB were
measured in fish from lakes Kuetsjarvi and Skrukkebukta
downstream from Nikel (Figure 3). The levels
in Vaggatem were significantly lower for all the analysed
fish species compared to lakes Kuetsjarvi and
Skrukkebukta. The highest levels were measured in
trout (14.9 ng/g ww) from Skrukkebukta. The levels of
PCBs in fish from Skrukkebukta from the present study
are higher compared to the results from the same
fish species in Skrukkebukta in 2008 (Christensen
2008). The reason for this increase is not known.
The levels of PCBs in fish from lakes Kuetsjarvi
and Vaggatem are elevated compared to other studies
from Northern Norway (Christensen et al. 2008).
The higher levels of PCBs in the analysed samples
from fish downstream from Nikel compared to
upstream clearly indicate emissions of PCBs from Nikel.
The PCBs’ source might be related to the activity
in the metallurgical smelter or in the Nikel city. PCBs
have historically been used as an insulating material
in electric equipment, such as transformers and capacitors,
and in fluids, lubricants, paints and plasticizers.
One possible source is landfills with PCB-containing
products that are leaking into the environment.
Pesticides
The results for ∑DDT were very similar to the findings
for ∑PCB with the highest average concentrations
in fish from lakes Kuetsjarvi and Skrukkebukta
downstream from Nikel (Figure 4). The levels in Vaggatem
were significantly lower for all the analysed fish
species compared to lakes Kuetsjarvi and Skrukkebukta.
The highest levels were measured in whitefish
(3.38 ng/g ww) and trout (3.12 ng/g ww) from Skrukkebukta.
The levels of DDT in fish from Skrukkebukta
from the present study are higher compared to the
results from the same fish species from 2004 and
2008 (Christensen et al. 2007, Christensen 2008).
The reason for this increase is not known but might
be related to small sample size in the previous studies.
The levels of DDT in fish from lakes Kuetsjarvi
and Skrukkebukta are considerably higher compared
to other studies from Northern Norway, while the levels
in Vaggatem are similar (Christensen et al. 2008,
Skotvold et al. 1997).
The concentrations of hexachlorobenzene (HCB),
hexachlorocyclohexane (HCH) and chlordanes were
slightly higher in fish downstream from Nikel but the
levels was comparable with other studies from Northern
Norway (Christensen et al. 2008).
Polybrominated diphenyl ethers (PBDEs)
The results for PBDEs were different from the results
for PCBs and DDT with the highest average concentrations
for PBDEs in perch (154 ng/kg ww) and trout
(149 ng/kg ww) from Skrukkebukta (Figure 5). The levels
in fish from this lake were considerably higher
compared to fish from Vaggatem. The levels of PBDE
in perch from Skrukkebukta were 10 times higher than
in Kuetsjarvi and 100 times higher than in Vaggatem.
The concentration of PBDEs in fish from Skrukkebukta
in the present study is higher compared to the
results from the same fish species in Skrukkebukta
in 2004 and 2008 (Christensen et al. 2008, Christensen
2008). The reason for this increase is not known
102

ut might be related to small sample size in the previous
studies. The levels of PBDE in fish from lakes
Skrukkebukta and Kuetsjarvi are higher compared to
other studies from Northern Norway (Christensen et
al. 2008).
0,6
Pasvik River, Hg
0,5
mg/kg ww
0,4
0,3
0,2
0,1
Vaggatem
Kuetsjavri
Skrukkebukta
0,0
Pike Perch SR DR
whitefish whitefish
Trout
mg/kg ww
Mercury in perch
1,6
1,4
1,2
1
0,8
0,6
0,4
0,2
0
10 15 20 25 30 35
Length (cm)
Kuetsjarvi
Skrukkebukta
Vaggatem
Figure 1. Upper: Levels of mercury (mg/kg ww) in fish tissue from Vaggatem
(Ruskebukta and Tjerebukta, blue bars), Lake Kuetsjarvi (yellow bars) and
Skrukkebukta (red bars). Lower: Levels of mercury (mg/kg ww) related to fish
length (cm) in fish tissue from Vaggatem (Ruskebukta and Tjerebukta, blue
dots), Lake Kuetsjarvi (yellow dots) and Skrukkebukta (red dots).
0,6
Nickel (Ni)
0,6
Copper (Cu)
0,5
0,5
mg/kg ww
0,4
0,3
0,2
Vaggatem
Kuetsjavri
Skrukkebukta
mg/kg ww
0,4
0,3
0,2
Vaggatem
Kuetsjavri
Skrukkebukta
0,1
0,1
0,0
Pike Perch SR DR
whitefish whitefish
Trout
0,0
Pike Perch SR DR
whitefish whitefish
Trout
Figure 2. Levels of nickel and copper (mg/kg ww) in fish tissue from Vaggatem (Ruskebukta and Tjerebukta, blue bars), Lake
Kuetsjarvi (yellow bars) and Skrukkebukta (red bars).
103

12
Total PCB
10
8
Figure 3. Levels of ∑PCB (ng/g ww) in fish tissue
from Vaggatem (Ruskebukta and Tjerebukta,
blue bars), Lake Kuetsjarvi (yellow bars) and
Skrukkebukta (red bars).
ng/g ww
6
4
2
0
Pike Perch SR DR
whitefish whitefish
Trout
Vaggatem
Kuetsjavri
Skrukkebukta
DDT
2,5
2,0
Figure 4. Levels of ∑DDT (ng/g ww) in fish tissue
from Vaggatem (Ruskebukta and Tjerebukta,
blue bars), Lake Kuetsjarvi (yellow bars) and
Skrukkebukta (red bars).
ng/g ww
1,5
1,0
0,5
0,0
Pike Perch SR DR
whitefish whitefish
Trout
Vaggatem
Kuetsjavri
Skrukkebukta
PBDE
160
140
120
Figure 5. Levels of ∑PBDE (ng/kg ww) in fish
tissue from Vaggatem (Ruskebukta and Tjerebukta,
blue bars), Lake Kuetsjarvi (yellow bars)
and Skrukkebukta (red bars).
ng/kg ww
100
80
60
40
20
0
Pike Perch SR DR
whitefish whitefish
Trout
Vaggatem
Kuetsjavri
Skrukkebukta
Conclusions and recommendations
• The highest levels of all detected legacy POPs were found in fish downstream from the Nikel city in lakes
Kuetsjarvi and Skrukkebukta.
• The highest concentrations of mercury were found in perch and pike from Skrukkebukta. The lowest levels
were found in fish from Lake Kuetsjarvi.
• The levels of POPs are considered to be elevated in lakes Kuetsjarvi and Skrukkebukta compared to other
lakes in the region.
• The higher levels of POPs and mercury in the analysed samples from fish downstream from Nikel compared
to upstream clearly indicate emissions of these compounds from Nikel. The source might be related to
the activity in the metallurgical smelter or to other activities or landfills in the Nikel city.
• There seems to be an increase in concentrations of mercury, PCB, DDT and PBDE in fish from lakes Kuetsjarvi
and Skrukkebukta since the previous studies in 2008.
• It is recommended that POPs and mercury are included in a future adaptive monitoring programme for the
Pasvik River.
104

References
AMAP 2000: PCB in the Russian Federation: Inventory and Proposals for Priority Remedial Actions. Executive Summary of
the report of Phase 1: Evaluation of the Current Status of the Problem with Respect to Environmental Impact and Development
of Proposals for Priority
AMAP, 2011: AMAP Assessment 2011: Mercury in the Arctic. Arctic Monitoring and Assessment Programme (AMAP), Oslo,
Norway. xiv+193 p. Remedial Actions of the Multilateral Cooperative Project on Phase-out of PCB Use, and Management
of PCB-contaminated Wastes in the Russian Federation. AMAP Report 2000:3.
AMAP 2011: AMAP Assessment 2011: Mercury in the Arctic. Arctic Monitoring and Assessment Programme (AMAP), Oslo,
Norway. xiv+193 p.
Amundsen, P.-A., Staldvik, F., Lukin, A., Kashulin, N., Reshetnikov, Y.S., Popova, O. 1993: Ecology and heavy metal contamination
in the fish communities of the Pasvik River system. Report. Norwegian College of Fishery Science, University of
Tromsø, Norway. 29p.
Amundsen, P.-A., Bøhn, T., Våga, G.H. 2004: Gill raker morphology and feeding ecology of two sympatric whitefish (Coregonus
lavaretus) morphs. Annales Zoologici Fennici 41: 291–300.
Amundsen, P.-A., Staldvik, F.J., Lukin, A., Kashulin, N., Popova, O., Reshetnikov, Yu. 1997: Heavy metal contamination in
freshwater fish from the border region between Norway and Russia. Science of the Total Environment 201: 211–224.
Arnesen, R., Traaen, T., Moiseenko, T., Kudravtseva, L., Mokrotovarova, O. 1996: Heavy metals from Nikel area. NIVA-
Report SNO 3526 – 96. Oslo: 37 p.
Aspholm, P. E. 2004: Fish and fishery resources in the Inari-Pasvik water system. Conference on integrated water management
of transboundary catchments: a contribution from transact 24–26 March 2004.
Aspholm, P.E., 1996: The Pasvik River. Barentswatch: 12–17.
Christensen, G., A. Evenset, S. Rognerud, B.L. Skjelkvåle, R. Palerud, E. Fjeld, O. Røyset 2008: National lake survey
2004–2006, PART III: AMAP. Status of metals and environmental pollutants in lakes and fish from the Norwegian part of
the AMAP region. Akvaplan-niva rapport 3613.01. SFT TA 2363-2008.
Christensen, G. 2008: Miljøgifter i fisk fra Pasvikvassdraget – nye undersøkelser fra 2008. Akvaplan-niva report 4732.01. (in
Norwegian)
Christensen, G.N., Andersen, H.J., Dahl-Hansen, G. 2015: Contaminants in fish from the Pasvik River. Akvaplan-niva
report. 6390.01.
Dauvalter, V. 1994: Heavy metals in lake sediments of the Kola Peninsula. Science of the Total Environment 158: 51–61.
Dauvalter, V., Rognerud, S. 2001: Heavy metal pollution in sediments of the Pasvik River drainage. Chemosphere 42: 9–18.
de March, B.G.E, de Wit, C.A., Muir, D.C.G. 1998: Persistent organic pollutants. In: AMAP Assessment Report: Arctic Pollution
Issues. Arctic Monitoring and Assessment Programme (AMAP). Oslo. Norway. 183–372.
Fjeld, E., Rognerud, S., Christensen, G., Dahl-Hansen, G., Veiteberg Braaten, H.–F. 2010: Miljøovervåking av kvikksølv i
abbor, 2010. NIVA rapport LNR 6090-2010. (in Norwegian)
Kashulina, T.G., Kashulin N.A. 1997: Accumulation and distribution of Ni, Cu, and Zn in the organs and tissues of fishes in
subarctic waters. Environmental Pollution of the Arctic. Tromso. Norway. 210–212.
Lukin, A., Dauvalter V., Kashulin, N., Yakovlev, V., Sharov, A., Vandysh, O. 2003: Assessment of copper-nickel industry
impact on a subarctic lake ecosystem. Science of the Total Environment 306: 73–83.
Moiseenko T.I. 1994: Acidification and critical loads in Surface Waters; Kola, Northern Russia. Ambio, 23(7): 418–424.
Moiseenko T.I., Kudryavtseva, L.P., Rodushkin, I.V., Lukin, A.A., Kashulin, N.A., Dauvalter, V.A. 1995: Airborne contaminants
heavy metals and aluminium in the freshwater ecosystems on the Kola subarctic region, Russia. Science of the
Total Environment 160–161: 715–727.
Skotvold, T., Wartena, E.M.M., Rognerud, S. 1997: Heavy metals and persistent organic pollutants in sediments and fish
from lakes in Northern and Arctic regions of Norway. Statlig program for forurensningsovervåkning, SFT rapport 688/97.
98p.
Stebel, K, Christensen, G. Derome, J., Grekelä, I. 2007: State of the environment in the Norwegian, Finnish and Russian
border area. Report. The Finnish Environment 6/2007.
Traaen, T.S., Henriksen, A., Moiseenko, T., Wright, R.F. 1992: Lake Monitoring, critical load of sulphur and modelling of
future acidity for several sulphur reduction scenarios in the Norwegian-Russian border areas. Symposium on the State of
Environment and Environmental Monitoring in Northern Fennoscandia and the Kola Peninsula. Arctic Centre Publications
No 4, 161–164. University of Lapland, Rovaniemi.
Traaen, T.S., Moiseenko, T., Dauvalter, V., Rognerud, S., Henriksen, A., Kudryavtseva L. 1991: Acidification of Surface
Waters, Nickel and Copper in Water and Lake Sediments in the Russian-Norwegian Border Areas. Progress Report for
1989–1990. Working Group for Water and Environmental Problems under the Norwegian-Soviet Environmental Protection
Commission. Oslo and Apatity.
105

106

Chapter 4: Evaluation and development
of the lake monitoring network
Photo: Helén Johanne Andersen
107

1 Introduction
The fifth activity dealt with the Pasvik area small lake
monitoring network, first presented in Stebel et
al. (2007). Water quality in the small lakes was last
reported in Ylikörkkö et al. (2014). Previously regular
monitoring has focused predominantly on chemical
quality. For 13 selected lakes, additional biological
elements are introduced here: phytoplankton,
periphytic diatoms, zoobenthos and fish. Uniform and
concurrent sampling between the three countries was
intended. Furthermore, to increase understanding of
the history of contaminations and paleolimnology of
the area, also sediment was analysed.
Sediment sampling, chemical and biological monitoring
was conducted in 2012–2013 and the resulting
data was used in assessment of variables in terms
of their reliability, cost-effectiveness and sensitivity to
changes in climate and harmful substances. The Pasvik
area small lakes monitoring programme was updated
in the light of the new information.
In analysis of chemical and biological state the lakes
were grouped into three geographical regions:
Vätsäri west of Nikel in Finland, ‘southern’ south of Nikel
in Russia and Jarfjord area in Norway. Lake Sierramjärvi
west of the actual Vätsäri area was included
in the biological monitoring programme. On Russian
side the lakes for biological sampling lie mainly south
of the Pasvik River and the Jarfjord area is north-east
of Nikel.
1 Lampi 222
2 Harrijärvi
3 Pitkä-Surnujärvi
4 Sierramjärvi
5 Pikkujarvi
Näätämö
!(
Kirkenes
!(
"J
18
13 14
"J "J
15
"J
16
"J
17
"J
6 Shuonijaur
7 Ala-Nautsijarvi
8 Ilja-Nautsijarvi
9 Toartesjaur
10 Virtuovoshjaur
4
"J
2
"J
"J
3
1
"J
River Pasvik
ZAPOLYARNY
5
!(
"J
!(
NIKEL
6
"J
11 Riuttikjaure
12 Kochejaur
13 Holmvatn
14 Gardsjøen
15 Rabbvatn
16 Durvatn
17 Børsevatn
Inari
!(
Ivalo
!(
!(
Nellim
9 "J
10
"J
11
"J
12
"J
7
"J "J
8
18 Rundvatn
0 50
km
© Maanmittauslaitos, lupa nro 7/MML/14
Figure 1. Location of the lakes under study (2012–2013). Lakes 1–4 are Finnish , 5–12 Russian and 13–18 Norwegian.
108

2 Water quality
JUKKA YLIKÖRKKÖ, GUTTORM N. CHRISTENSEN, HELÉN JOHANNE ANDERSEN
Methods
Water samples were taken between June and September
in 2012 and 2013 (Table 1). The analysis of
water quality served as a background for the biological
variables and sediment analysis. Specific chemical
analysis methods for each country are introduced
in Puro-Tahvanainen et al. (2008). When available,
previous data from the same area was included in
analysis for more reliable interpretation. Jarfjord area
samples were all from 1 meter. For the other regions,
average total nutrient content (tot. N, tot. P), total organic
carbon (TOC) content and alkalinity concern
1–10 m water column. Salinity balance and metal
concentrations were calculated from samples from
the whole water column. Values below the reliable detection
limit were calculated as half of the limit value.
Results and discussion
Nitrogen and phosphorus
Total phosphorus was on average below the reliable
detection limit (2 µg/l) or up to 3 µg/l in Vätsäri, 3–9
µg/l in the Russian lakes and 3 µg/l in Jarfjord (Figure
1). The southernmost lakes in Russia differed from
others with the highest total phosphorus content.
Total nitrogen concentrations varied greatly within
the areas (Figure 2). On average nitrogen concentrations
were 90–170 µg/l in Vätsäri, 135–208 µg/l in the
southern lakes and 94–180 µg/l in the Jarfjord lakes.
Southern lakes stood out with slightly higher average
nitrogen content.
The observed nutrient concentrations are typical
for the lakes, considering their location. The Vätsäri
and Jarfjord lakes are the poorest in nutrients. The
more southern lakes lie on lower altitude, deeper in
the forest zone, where there are thicker, more organic
soils, resulting in typically higher trophic status in the
lakes. The previous results from the lakes or regions
indicate the same nutrient levels (Puro-Tahvanainen
et al. 2011, Kashulin et al. 2008).
Table 1. Lake area (km 2 ), altitude (masl) and number of water
samples (N) during the years within the project time frame and
previous data used in analysis.
Lake km 2 masl Years N
Finland Lampi 222 0.2 222 2013 3
2012–2000 3
Harrijärvi 1.0 127 2013 2
2012–2000 2
Pitkä-Surnujärvi 0.7 126 2013 2
Sierramjärvi 1.1 254 2013 4
2012–2000 10
Russia Pikkujärvi
Shuonijaur 11.3 180 2012–2013 6
2011–2000 4
Ilja-Nautsijarvi
Ala-Nautsijarvi 3.2 133 2012 1
Toartesjaur 0.6 195 2013 2
Virtuovoshjaur 1.3 182 2012–2013 6
2011–2000 3
Riuttikjaure 0.9 190 2013 1
Kochejaur 18.5 159 2011–2000 9
Norway Gardsjøen 0.7 82 2013 1
2012–1995 3
Holmvatn 0.8 156 2013 1
2012–2000 2
Rabbvatn 0.4 83 2013 1
2012–2000 2
Durvatn 0.4 231 2013 1
2012–1993 2
Børsevatn 0.4 178 2013 1
Rundvatn
109

P tot. µg/l
14
12
10
8
6
4
2
0
P tot. µg/l
14
12
10
Vätsäri
8
Jarfjord
6
Russia
4
2
0
Vätsäri
Jarfjord
Russia
Figure 1. Average total phosphorus concentration and its standard deviation for each lake and area.
300
250
300
250
N tot. µg/l
200
200
150
150
Vätsäri
100
Jarfjord 100
50 Russia
50
N tot. µg/l
0
0
Vätsäri
Jarfjord
Russia
Figure 2. Average total nitrogen concentration and its standard deviation for each lake and area.
TOC mg/l
8
7
6
5
4
3
2
1
0
TOC mg/l
8
7
6
5
4
Vätsäri
Jarfjord 3
Russia 2
1
0
Vätsäri
Jarfjord
Russia
Figure 3. Average concentration and standard deviation of total organic carbon for each lake and area.
110

Organic matter
Total organic carbon (TOC) followed similar pattern
across the regions as nutrients. It was the lowest in
the Vätsäri and Jarfjord lakes: on average 1.3–2.3
mg/l and 1.3–3.3 mg/l, respectively (Figure 3). Among
the Russian lakes, TOC varied widely from 4.0 to 6.4
on average, depending of the lake. TOC content in the
Russian lakes was clearly higher than in the other two
regions, and Lake Virtuovoshjaur stands out with the
highest measured values (Figure 3).
Alkalinity and pH
Alkalinity average was the lowest 68 µeq/l in Lampi
222 in Vätsäri and the highest 217 µeq/l in Kochejaur,
Russia (Figure 4). Generally the lakes in Vätsäri,
excluding Sierramjärvi, were the least alkaline. The
more southern lakes were slightly more alkaline.
There was little variation in the lake’s pH values:
the lowest 6.6 were measured in Pitkä-Surnujärvi
(Vätsäri) and Holmvatn (Jarfjord) and highest 7.2 in
Kochejaur in Russia (Figure 5). On regional level Vätsäri
and Jarfjord had average pH just below 6.8, only
slightly lower than southern region (Figure 5).
Alkalinity and pH were within the same range as
reported earlier for Vätsäri (Puro-Tahvanainen et al.
2011) and some of the Russian lakes (Kashulin et al.
2008). The selected Jarfjord lakes appeared to have
more neutral water compared to some other small lakes
in the area described in Puro-Tahvanainen et al.
(2011). No clear indication of acidification was observed
in water samples of the studied lakes. All the regions
have rather low alkalinity naturally.
250
250
200
200
Alk. µeq /l
150
100
50
Alk. µeq /l
150
100
50
Vätsäri
Jarfjord
Russia
0
0
Figure 4. Average alkalinity for each lake and for each area with standard deviation.
pH
7,6
7,4
7,2
7,0
6,8
6,6
6,4
pH
7,6
7,4
7,2
7,0
6,8
Vätsäri
Jarfjord
6,2
6,0
5,8
6,6
6,4
6,2
Russia
6,0
Figure 5. Average pH and its standard deviation for each lake and area.
111

Salinity balance
Cation content tended to be low in the Vätsäri lakes
(Table 2, Figure 6). Lake Shuonijaur, in the vicinity of
Nikel, stands out with higher calcium (2.0 mg/l) and
magnesium (0.8 mg/l) concentrations. The southern
lakes in Russia had mostly higher cation contents and
greater variance between the lakes. Jarfjord lakes were
roughly on the same level with calcium (Ca), magnesium
(Mg) and potassium (K), but sodium (Na) content
was much higher.
Average sulphate ranged 1.9–2.1 mg/l in Vätsäri,
1.8–3.3 in the Russian lakes, and on notably higher
level 2.1–4.7 in Jarfjord (Figure 7). The greatest difference
was between Vätsäri and Jarfjord lake sulphate
levels. Chloride content did not differ much between
Vätsäri (0.8–1.4 mg/l) and the Russian lakes (0.9–1.7
mg/l) (Figure 8). Chloride was much higher in Jarfjord
compared to the other two regions.
Being closest to the ocean, the Jarfjord lakes receive
more marine salts, which shows in higher sodium
and chloride contents in the area. Sulphate concentrations
in Jarfjord are elevated also by sulphur deposition
from the Pechenganikel. These ions contribute
to salinity, and due to more salts the conductivity is
notably higher in the Jarfjord region (Figure 9).
Table 2. Average range and regional means for main cations (mg/l) in the lakes of three regions.
Ca (mg/l) Na (mg/l) Mg (mg/l) K (mg/l)
range mean range mean range mean range mean
Vätsäri 1.2–2.0 1.4 1.4–1.5 1.5 0.3–0.8 0.5 0.2–0.3 0.2
South 1.8–3.1 2.3 1.2–1.5 1.4 0.9–1.1 0.9 0.4–0.6 0.5
Jarfjord 1.6–2.7 2.0 2.8–3.9 3.3 0.8–1.1 0.9 0.3–0.4 0.4
3
4
Ca mg/l
2,5
2
1,5
1
0,5
Vätsäri
Jarfjord
Russia
Na mg/l
3,5
3
2,5
2
1,5
1
0,5
Vätsäri
Jarfjord
Russia
0
0
1,2
0,6
1
0,5
Mg mg/l
0,8
0,6
0,4
Vätsäri
Jarfjord
Russia
K mg/l
0,4
0,3
0,2
Vätsäri
Jarfjord
Russia
0,2
0,1
0
0
Figure 6. Regional average values and standard deviations for calcium (Ca), sodium (Na), magnesium (Mg) and potassium (K).
112

SO 4 mg/l
6
5
4
3
2
1
0
4 mg/l
6
5
4
3
Vätsäri
2
Jarfjord
SO 43
Russia
1
0
Vätsäri
Jarfjord
Russia
Figure 7. Average concentration and standard deviation of sulphate for each lake and area.
7
5,0
Cl mg/l
6
5
4
3
2
1
Vätsäri
Jarfjord
Russia
Conductivity mS/m
4,0
3,0
2,0
1,0
Vätsäri
Jarfjord
Russia
0
0,0
Figure 8. Regional average values and standard deviations for
chloride
Figure 9. Regional average values and standard deviations for
conductivity.
Metals
Average nickel concentration was the lowest in Vätsäri:
0.4–1 µg/l (Figure 10). In the Russian lakes it
was roughly on the same level (0.5–1 µg/l), excluding
Lake Shuonijaur close to Nikel, where average nickel
concentration was 8.8 µg/l. Jarfjord region had the
highest nickel concentrations varying (4.9–16.7 µg/l).
Copper concentrations followed a similar pattern
(Figure 11). Average copper in Vätsäri and the Russian
lakes varied 0.5–1.3 µg/l, excluding Shuonijaur
(4.9 µg/l). Also the copper levels were the highest in
the Jarfjord lakes: 2.5–9.2 µg/l.
Copper and nickel deposition from the Pechenganikel
show in Shuonijaur close to Nikel, and in the
Jarfjord lakes directly downwind from the smelter.
Both metals have notably elevated concentrations in
these lakes. Metal pollution is on the same level in
these Jarfjord lakes as reported for other lakes earlier
in Puro-Tahvanainen et al. (2011). First time sampled
Børsevatn appears to be the most polluted of all the
lakes.
Cadmium concentrations were low in all the lakes.
In Vätsäri area measured cadmium was below the reliable
detection limit 0.01 µg/l and on the same level
in Jarfjord lakes, excluding Rabbvatn (0.02 µg/). Average
cadmium in the Russian southern lakes was only
slightly more: 0.03–0.05 µg/l.
Lead concentrations in Vätsäri lakes varied between
0.02–0.04 µg/l, on average. The other regions
had significantly higher lead content: in Jarfjord the
corresponding range was 0.02–0.52 µg/l, and in the
southern lakes 0.13–0.23 µg/l.
Average iron concentrations varied widely: 4–11
µg/l in Vätsäri, 12–284 µg/l in the southern lakes and
10–49 in Jarfjord. Relative to the average, the measured
iron contents varied highly within the sampling period.
113

Ni µg/l
18
17
16
10
9
8
7
6
5
4
3
2
1
0
Ni µg/l
10
9
8
7
6
5
4
3
2
1
0
Vätsäri
Russia
Jarfjord
Figure 10. Average nickel concentration and standard deviation for each lake and area.
Cu µg/l
10
9
8
7
6
5
4
3
2
1
0
Cu µg/l
10
9
8
7
6
5
Vätsäri 4
Jarfjord
3
Russia
2
1
0
Vätsäri
Jarfjord
Russia
Figure 11. Average copper concentration and standard deviation for each lake and area.
References
Kashulin, N.A., Dauvalter, V.A., Sandimirov, S.S., Terentjev, P.M., Koroleva, I.M. 2008: Catalogue of lakes in the Russian,
Finnish and Norwegian border area. 141 p.
Puro-Tahvanainen, A., Zueva, M., Kashulin, N., Sandimirov, S.,Christensen, G.N., Grekelä, I. 2011 Pasvik Water Quality
Report Environmental Monitoring Programme in the Norwegian, Finnish and Russian Border Area. Centre for Economic
Development, Transport and the Environment for Lapland, Publications 7/2011. 52 p.
Puro-Tahvanainen, A., Grekelä, I., Derome, J., Stebel, K. (ed) 2008: Environmental monitoring programme in the Norwegian,
Finnish and Russian border area – Implementation guidelines. 51 p.
Stebel, K., Christensen, G., Derome, J., Grekelä, I. (ed) 2007: State of the nvironment in the Norwegian, Finnish and Russian
Border Area. The Finnish Environment 6/2007. 98 p.
Ylikörkkö, J., Zueva, M., Kashulin, N., Kashulina, T., Sandimirov, S., Christensen, G., Jelkänen, E. 2014: Pasvik Water
Quality until 2013 – Environmental Monitoring Programme in the Norwegian, Finnish and Russian Border Area. Centre for
Economic Development, Transport and the Environment for Lapland, Publications 96/2014. 43 p.
114

Jarfjord mountain. Photo:Helén Andersen
Going to sampling in Vätsäri. Photo: Jouni Satokangas
115

3 Sediments and paleolimnology
VLADIMIR DAUVALTER, DMITRII DENISOV
The border area between Russia, Norway, and Finland
is exposed to serious industrial impact. Lake
Kuetsjarvi and the lower watercourse of the Pasvik
River receive wastewater from smelting and by-processes
of the Pechenganikel Company. The river,
as well as lakes and rivers of this area not part the
Pasvik watercourse, is also exposed to pollution via
atmospheric deposition. The major pollutants include
sulfur compounds and heavy metals (Ni, Cu, Cd, Cr,
Zn, As, Hg, etc.), polycyclic aromatic hydrocarbons
(PAHs), and persistant organic pollutants (POPs).
Sulfur dioxide emissions cause acidification and pollution
of the surface water due to intensification of rock
weathering processes. Dust, nitrogen oxides and carbon
dioxides are also part of the emissions from the
Pechenganikel.
The history of ecosystems’ development and the
range of natural fluctuation of biological parameters
are needed to identify the reasons for various current
changes in the Pasvik watercourse. Information about
specific past environmental features allows identification
of the role of climate change in the ecosystems’
transformations, which is especially important in the
analysis of industrial impact on the northern areas.
In this context the small lakes of glacial origin in the
river’s catchment area are most suitable for obtaining
paleoecological information for reconstruction of the
historical dynamics of the environment and climate.
Layer-by-layer analysis of sediments provides data
on global and local climate changes, fluxes of various
substances into the environment etc., which can be
interrelated with absolute time scale. Paleoecological
research and restoring of water ecosystems’ development
history is impossible without correct estimation
of sedimentation rate allowing to determine the age of
studied sediments. Radionuclides are widely used for
determination of sedimentation rate in natural water
reservoirs. 210 Pb is applicable for studies of contemporary
sedimentation rates (from 100 to 150 years)
which in turn can be used to estimate the age of sediments.
210 Pb-dating is often used for chronological
reconstructions of anthropogenic pollution by radionuclides,
heavy metals and organochlorides.
Materials and methods
Sediment cores from the deepest areas were sampled
from the 16 lakes (Introduction, Figure 1). Heavy
metal concentrations of the sediments were analyzed
and the level of industrial load on the lakes’ ecosystems
was determined according to the estimated pollution
factor (C f
) for each of the priority heavy metal
contaminant (method of Håkanson, 1980). The contamination
degree (C d
) of the bottom sediment was
determined by the sum of all C f
values for eight main
heavy metals in a particular lake.
Three lakes were selected for the study of diatom
complexes of bottom sediments (Harrijärvi, Finland,
Rabbvatnet, Norway, and Shuonijaur, Russia) (Introduction,
Figure 1). The selected lakes are characterized
by depths sufficient for formation of uninterrupted
sequence of layers of bottom sediments.
Diatom analysis was carried out according to the
standard generally accepted method (Zhuze et al.
1949, Davydova 1985, Denisov et al. 2006). All diatom
valves were identified, if possible, to intraspecific taxonomic
categories (Krammer & Lange-Bertalot 1988–
2003). Further analysis included investigation of taxonomic
structure of diatom complexes, dynamics of
relative abundance (%) of the predominant species
and estimation of the total amount of valves in the sediment.
Species diversity was estimated according to
Shannon-Weaver index (H’ bit/ex.). The total amount
of valves was counted in each investigated layer (mln.
ex./g).
Tolerance analysis in relation to pH was made for
the discovered taxa and integral pH value for each
layer was calculated using the equation (Moiseenko
& Razumovsky 2009): pH = ∑ph i
k / ∑k, where ph i
is
the individual numerocal value of each indicator taxon
and k is the relative abundance of this taxon.
In relation to pH values the following plankton
groups were identified: neutrofilic with developmental
optimum at pH 7.0, indifferent capable of developing
in a relatively wide range of pH around 7, alkalifilic
preferring pH> 7.0, alkalibiont preferring pH 7.6 and
above, acidofilic preferring pH< 7.0 and acidobiont developing
at relatively low pH 6.4 and below.
116

Ecological groups of diatoms and their proportions
in each sediment layer were analyzed to reconstruct
the conditions in each lake. According to preferred habitat
phytoplakton was divided into planktic, benthic
and plankto-benthic forms. The relation to water salinity
(chloride concentration) was also considered:
halophob (growth is inhibited by salinity), indifferent
(tolerates different salinities), oligohalob (tolerates some
salinity), halofilic (growth is stimulated by salinity)
and mesohalob (grows in water with medium salinity,
for example in brackish water) groups were identified.
Last ecological group was formed according to biogeographical
association (cosmopolitan, arctic-alpine,
boreal, holarctic).
Saprobity index (S) was calculated for each analyzed
layer as an indicator of presence of nutrients and
also as an indirect indicator of the lakes’ trophic status
(Sladecek 1967, Barinova et al. 2006). Data of ecology
of specific algae taxa, individual saprobity indicators
and reaction to pH from the updated database on
algae ecology (Barinova et al. 1996, 2006) were used
in the analysis.
Results and discussion
Sediments
Lead-based method of sedimentation rate determination
Sediment cores’ ages were determined according to
210
Pb chronology and the sedimentation rates (Table
1). The average rate of sedimentation in the latest
hundred and fifty years in the lakes was fairly stable,
within 0.3–0.6 mm/year. The lowest sedimentation rates
are typical for the oligotrophic lakes Rabbvatnet,
Virtuovoshjaur and Shuonijaur. Rabbvatnet had the
longest sediment column and oldest sediment, which
allows the study of environmental and climate changes
prior to active industry in the catchment area of
the Pasvik River.
Background concentrations of elements in bottom
sediments
Background heavy metal concentrations are found
in the deepest layers of the sediment cores (usually
>20 cm) which were formed over two hundred years
ago, i.e. before industrial development of North Fennoscandia
(Norton et al. 1992, 1996, Rognerud et al.
1993). The background heavy metal concentrations
reflect the geochemical peculiarities of the catchment
and provide quantitative information of water bodies’
pollution degree and help determine anomalies
in search of mineral resources (Tenhola & Lummaa
1979).
The maximum background heavy metal concentrations
in sediment were found in different lakes: Cu and
Hg in Lake Lampi 222, Zn and Cd in Lake Ala-Nautsijarvi,
Co in Lake Ilja-Nautsijarvi, Ni in Lake Pikkujarvi,
Pb in Lake Holmvatnet, and As in Lake Gardsjøen.
Factor analysis was used to identify the factors having
the greatest impact on the chemical composition of
the lakes’ sediment. Analysis confirmed the influence
of geochemical composition of bedrock on the formation
of the sediment background layers’ chemical
composition.
Cluster analysis identified three groups of water
bodies: the first group includes the Finnish lakes (Pitkä-Surnujärvi
and Sierramjärvi) and a similar Russian
lake Ilja-Nautsijarvi, the second group includes the
Russian lakes Shuonijaur and Virtuovoshjaur and the
Norwegian lake Rabbvatnet, and the third group includes
the Russian lakes Toartesjaur, Kochejaur and
Riuttikjaure. The lakes in the groups are believed to
be similar in terms of the natural conditions of sediment
chemistry formation. A large number of lakes not
belonging to any of the three groups shows the large
diversity of these conditions, which is reflected by a
considerable range of background concentrations in
Table 1. The lengths of sediment cores, sedimentation rates and the ages of the bottom sediments of some lakes.
Lake
core length
(cm)
sedimentation rate
(mm/year)
Approx. age of bottom sediment
(years)
Rabbvatnet 44 0.65 687
Harrijarvi 30 1.3 240
Shuonijaur 14 0.7 210
Ala-Nautsijarvi 17,5 1.6 106
Virtuovoshjaur 18 0.7 257
Kochejaur 16 1.5 106
117

sediments. It was also established that the average
background concentrations in sediments are similar
to average concentrations of chemical elements in
the crust of earth (percentage abundance) and in the
rocks and soils.
Changes of elements’ concentration in sediments
over time
Increased concentrations and sedimentation rates of
Ni, Cu, and Co in the sediments dated back to 20 th
century were observed in the Norwegian lakes (Norton
et al. 1992, 1996, Rognerud et al. 1993). Higher
concentrations of Pb in the sediments, generally, are
not related to the Pechenganikel’s emissions since
they date back to the time too early to be related to
any industrial activities in this area. Ni, Cu, Co and
other heavy metals enter the atmosphere from the
Pechenganikel but growth of the concentrations and
accumulation rates dates back one decade prior to
the beginning of the industrial activities.
Vertical distribution of heavy metal concentrations
in the sediments was studyied to find out the anthropogenic
load intensity of lakes located at different
distances from smelters. The most polluted with Cu
and Ni are the Russian lakes (Pikkujarvi, Shuonijaur),
located close to the emission source as well as all
Norwegian lakes of Jarfjord, exposed to intensive atmospheric
pollution of the integrated plant (Figure1).
Growth of Cu and Ni content in the surface layers of
sediment was also recorded in lakes Ilja-Nautsijarvi
and Virtuovoshjaur, located 80 and 90 km from the
smelters respectively.
Increasing Cu and Ni content in sediment cores
was recorded mainly near the integrated plant (Shuonijaur).
In the Norwegian lake Rabbvatnet the first
noticeable growth of Cu and Ni content was observed
in the middle of the 17 th century, which is probably associated
with the beginning of the industrial revolution
in Europe and increase of heavy metal emissions into
the environment and their air transport in the direction
of the Arctic (Figure 2).
The next noticeable growth of Cu content was observed
in the 19 th century, which may be attributed to
the industrialization. Since then the Cu concentration
has kept growing and the intensive growth of Cu content
is associated with the beginning of copper and
nickel production in the Pechenga area. In the last
two decades the production dropped after the collapse
of the USSR, but the concentrations of Cu in the
sediment of Lake Rabbvatnet (as well as in Russian
lakes Shuonijaur and Virtuovoshjaur) have only been
growing, which can be explained by heavy metal accumulation
in the catchment of the lakes (Dauvalter
et al. 2012).
No increase of Zn concentrations have been noted
in the surface layers except for Lake Pikkujarvi. In the
majority of the lakes there is a tendency of Zn decrease.Chalcophile
high-toxic heavy metals Cd and Pb
have been considered a global pollution element by
many ecologists in the last decades (for example Pacyna
& Pacyna 2001). Significant growth of Cd and Pb
concentrations in the dated sediment was recorded in
the beginning of the 20 th century associated with the
intensive industrial development after World War II,
metallurgical production at the Pechenganikel and the
growing use of leaded petrol in case of Pb. In the majority
of the lakes there is a tendency to both Cd and
Pb content growth towards the sediment surface, and
at the same time a decrease occurs in the very top
layer in about half of the lakes. Cd and Pb content reduction
in the surface layer dated to one-two decades
may be associated with the the collapse of the USSR
or with the reduction of global emission of Cd over the
last decades. Possibly the main reason for decrease
of Pb in the latest decades is the prohibition of leaded
petrol. However, in some lakes maximum heavy metal
contents are noted in the surface layer.
In majority of the lakes growth of chalcophile hightoxic
As and Hg content is noted towards their surface.
Especially noticeable growth of As and Hg occurred
in the middle of the latest century associated with
the industrial development after World War II, growing
use of As and coal high in Hg in metallurgy including
at the Pechenganikel Company. In the very top layer
of sediment (1–3 cm) of a few lakes a reduction of As
and Hg occurs, which is suggestive of the consequences
of the global emission reduction. The reduction
may be associated with the collapse of the USSR and
reductions of the global emissions in the latest decades
due to use prohibitions and increase in recycling.
Growth of Co concentrations was found in the surface
layers of all Norwegian lakes, Russian lakes located
close to Pechenganikel (Pikkujarvi and Shuonijaur)
and Finnish Lake Lampi 222, which is the closest
to the Pechenganikel among all the Finnish lakes. Other
studied lakes show the tendency of Co content
reduction towards the sediment surface. Co concentrations
reached maximum values in the beginning of
the 21 st century and then they drop towards the sediment
surface probably due to the reduction of production
of heavy metals.
118

Cu
0 100 200
0
10
20
30
40 Lampi
Cu
0 10 20 30
0
10
20
30
40 Harrijarvi
Cu
0 20 40 60
0
10
20
30
40 Pitkasurnujarvi
Cu 0 10 20 Ni 0 20 40 60
0
0
10
20
30
10
20
30
Ni
10
20
30
0 20 40 Ni 0 20 40 60 Ni 0 20 40
0
0
0
40 Sierramjarvi 40
Lampi 40 Harrijarvi 40 Pitkasurnujarvi 40 Sierramjarvi
10
20
30
10
20
30
Cu
Cu
Cu
0 20 40 60
0
10
20
30 Shuonijaur
0 10 20 30
0
10
20
30 Toartesjaur
0 100 200 300
0
10
20
30
40
50
Ref
Gardsjøen
Cu 0 20 40 60 Cu 0 10 20 30 Cu 0 100 200 300 Ni 0 40 80
0
Cu
0
10
20
30
AlaNautsijarvi
0 10 20 30 Cu
0
10
20
30 Virtuovoshjaur
Cu 0 100 200
0
10
20
30
40
Ref
Cu
0
10
20
30 IlaNautsijarvi
0 10 20 30
0
10
20
30 Riuttikijaure
0 100 200 300
0
10
20
30
40
10
20
30
Cu
30
Cu
50 Holmvatn 50 Rabbvatnnet 50
10
20
10
20
30
40
0
0
0
Pikkujarvi
0 10 20
Kochejaur
0 200 400
Ref
Durvatn
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Ni
Ni
10
20
Ni
10
20
0 20 40 60
0
0 20 40
0
10
20
30 Shuonijaur 30 AlaNautsijarvi 30 IlaNautsijarvi 30 Pikkujarvi
0 10 20
0
10
20
30 Toartesjaur
0 100 200
0
10
20
30
40
Ref
Ni
10
20
0 20 40
0
Ni
Ni
10
20
Ni
10
20
0 200 400
0
0 20 40 60 Ni 0 20 40
0
0
30 Virtuovoshjaur 30 Riuttikijaure 30 Kochejaur
Ni 0 100 200 Ni 0 100 200 Ni 0 200 400 600
0
10
20 30
40
Ref 0
10
20
30
40
0
10
20 30
40
Ref
50 Gardsjøen 50 Holmvatn 50 Rabbvatnnet 50 Durvatn
10
20
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Sediment depth, cm
Figure 1. Vertical distribution of Cu and Ni concentrations (µg/g of dry weight) in the sediments of the studied lakes. In this and in further figures the Finnish lakes are colored in blue, the Russian lakes in
red and the Norwegian lakes in green.
119

Cu 0 20 40 60 Cu 0 100 200 300 Cu 0 10 20 30 Ni 0 40 80
2050
2000
1950
1900
1850
1800
1750
1700
1650
1600
1550
1500
1450
1400
1350
1300
Shuonijaur
2050
2000
1950
1900
1850
1800
1750
1700
1650
1600
1550
1500
1450
1400
1350
1300
2050
2000
1950
1900
1850
1800
1750
1700
1650
1600
1550
1500
1450
1400
1350
Rabbvatnnet 1300
Harrijarvi
2050
2000
1950
1900
1850
1800
1750
1700
1650
1600
1550
1500
1450
1400
1350
1300
Shuonijaur
Cu 0 20 40 60 Cu 0 10 20 30 Cu 0 10 20 Ni 0 20 40 60
2050
2000
1950
1900
1850
1800
1750
1700
1650
1600
1550
1500
1450
1400
1350
1300 AlaNautsijarvi
2050
2000
1950
1900
1850
1800
1750
1700
1650
1600
1550
1500
1450
1400
1350
1300 Virtuovoshjaur
2050
2000
1950
1900
1850
1800
1750
1700
1650
1600
1550
1500
1450
1400
1350
1300
Kochejaur
2050
2000
1950
1900
1850
1800
1750
1700
1650
1600
1550
1500
1450
1400
1350
1300 AlaNautsijarvi
Figure 2. Vertical distribution of Cu and Ni concentrations (µg/g of dry weight) in the dated sediment of the studied lakes.
Ni
2050
2000
1950
1900
1850
1800
1750
1700
1650
1600
1550
1500
1450
1400
1350
1300
Ni
2050
2000
1950
1900
1850
1800
1750
1700
1650
1600
1550
1500
1450
1400
1350
1300
0 100 200
Rabbvatnnet
0 20 40
Virtuovoshjaur
Ni
2050
2000
1950
1900
1850
1800
1750
1700
1650
1600
1550
1500
1450
1400
1350
1300
Ni
2050
2000
1950
1900
1850
1800
1750
1700
1650
1600
1550
1500
1450
1400
1350
1300
0 20 40
Harrijarvi
0 20 40
Kochejaur
120

Territorial distribution of elements in the surface
of the bottom sediments
Long-term anthropogenic load on the catchments of
the lakes has led to alteration of natural conditions of
the formation of sediment chemical composition and
to growth of heavy metal concentrations in the surface
sediment layers. The main reason for high concentrations
of heavy metals (Ni, Cu, Co) in the surface
layers are the atmospheric emissions from the
smelters of the Pechenganikel especially near the integrated
plant. The majority of heavy metals from the
emissions and waste water are tied to and stay in the
sediments.
The most polluted lakes are located in the immediate
proximity of the smelters and in Jarfjord inside
20–40 km zone from the Pechenganikel. The highest
concentrations of Ni, Cu and Co, exceeding background
values by 5–25 times, were noted in the lakes
at a distance up to 40 –50 km from the integrated plant
(Figure 3). Further away at 60–100 km the reduction
of concentrations (first of all of Ni and Cu) is noted. A
similar pattern is observed in the distribution of Cd,
As, and Hg: the same area up to 50 km is polluted
most intensively (concentrations exceed background
values by 2–8 times) and at distance of >50 km the
concentrations are reduced to 1–3 times of the background
values. However, pollution with chalcophile elements
(especially Pb) is quite serious there.
The contents of Zn in the sediments exceed the
background values insignificantly (up to 1.4 times in
Lake Pikkujarvi); therefore this element can be regarded
as non-polluting. In the distribution of Pb no
increasing tendency in the surface layers was noted
approaching the Pechenganikel (the largest Pb concentrations
were recorded in the Norwegian territory),
which testifies that the Pechenganikel is not the major
source of Pb pollution.
A close relationship between the content of alkaline
and alkaline-earth metals (first of all K, Na and Mg) in
the sediment surface and the distance from the source
of pollution was also noted: concentrations grow
clos to the Pechenganikel. This means that along with
the emissions of heavy metals, the integrated plant also
releases into the atmosphere alkaline and alkalineearth
metals, which are in the ore-hosting and stripping
rocks containing minerals with large content of
these elements.
Factor analysis confirmed the contribution of the
Pechenganikel in the emissions of Cu, Ni, chalcophile
elements and alkaline and alkaline-earth metals that
greatly influence the chemical composition of the sediments.
Chalcophile elements are delivered also via
atmospheric transboundary transport. Physical and
chemical conditions of the lakes also influence the sediments
as does the water level in lakes above sea
level.
Cluster analysis clearly identified three groups of
water bodies: lakes Harrijärvi, Ilja-Nautsijarvi and Virtuovoshjaur
with relatively small contents of priority
pollutant heavy metals (Ni, Cu), lakes Shuonijaur and
Rabbvatnet with relatively high concentrations of Ni
and Cu and other heavy metals and Russian lakes Toartesjaur,
Riuttikjaure and Kochejaur, located close to
each other and far from the Pechenganikel integrated
plant (>80 km) and characterized by relatively small
contents of Ni and Cu as well as other heavy metals,
except for Pb and Hg in Lake Riuttikjaure. Probably
these clusters are similar by natural and anthropogenic
conditions of the sediments’ chemical composition
formation. The unclustered lakes suggest a great
variety of these conditions, which in itself reflects the
wide range of the elements’ contents in the sediment
surface of the studied lakes.
Factor and degree of contamination
To assess the geoecological condition of the surface
waters the factor and degree of contamination were
determined with the method of Håkanson (1980).
The contamination factor (C fi
) was calculated as the
quotient by divisiding the element (or compound) concentration
in the surface 1 centimeter layer by the
background value. The contamination degree (C d
)
was determined as the sum of the pollution factors for
all eight investigated pollution elements.
The following classification of contamination factor
was used: C fi

C f
value for Cd (3.1 – ‘significant’) was noted in Lake
Durvatn. The largest C f
values for Pb were also noted
in Norwegian lakes Rabbvatnet and Durvatn (14.3
and 13.0, respectively). Maximum C f
values for As
and Hg were recorded in lakes Sierramjärvi and Durvatn,
respectively (9.6 and 8.3). The pollution factors
for Pb, As and Hg refer to high degree of pollution. In
general the high pollution factors for Ni and Cu are
observed near the Pechenganikel smelters. Chalcophile
Pb, As and Hg are also recorded at a significant
distance from the smelters, which confirms the conclusions
regarding the global nature of pollution with
these high-toxic elements.
600
Ni, µg/g
600
Cu, µg/g
400
400
200
y = 4285x -1.0041
R 2 = 0.621
200
y = 6014.5x -1.174
R 2 = 0.5823
0
0
0
40 80 120 0 40 80 120
Distance from Nikel, km
Distance from Nikel, km
Cd, µg/g
Pb, µg/g
0.8
y = 0.3188x 0.0617
R 2 = 0.0069
60
40
y = 20x 0.0581
R 2 = 0.0082
0.4
20
0
0
0 40 80 120 0 40 80 120
Distance from Nikel, km
Distance from Nikel, km
As, µg/g
Hg, µg/g
30
20
y = 23.808x -0.343
R 2 = 0.0868
0.4
y = 0.3414x -0.2014
R 2 = 0.0373
10
0.2
0
0
0 40 80 120 0 40 80 120
Distance from Nikel, km
Distance from Nikel, km
Figure 3. Distribution of the
concentrations of key pollution
elements (µg/g) in the surface
layer (0–1 cm) of the sediment of
the studiedlakes depending on the
distance from the Pechenganikel
integrated plant.
200
100
Zn, µg/g
y = 98.869x -0.0272
R 2 = 0.0037
-0.56 86
y = 151.67x
R 2 = 0.1392
0
0
0 40 80 120 0 40 80 120
Distance from Nikel, km
Distance from Nikel, km
120
80
40
Co, µg/g
122

Sediment sampling. Photo: Guttorm Christensen
123

Paleolimnology
Lake Shuonijaur
In Lake Shuonijaur the diatom complexes in the top
layer of sediments (interval 0.5–1 cm) and in the bottom
part of the column (13–14 cm) were analyzed.
Top layer reflects the present-day status of the lake
ecosystem and the bottom layer the status before
the intensive industrial transformations (ca. 200 years
ago). Lake Shuonijaur is a subarctic lake with low
salinity and oligotrophic status. Diatom periphyton is
found on the numerous shallow-water areas on littoral
rocks and rocky bottom down to 2.5–3 m depth, which
illustrates high transparency of the water.
In the present-day plankton the typical species
are Aulacoseira alpigena (Grun.) Kramm., A. distans
(Ehrb.) Simons., Cyclotella schumannii (Grun.) Håk.,
C. rossii Håk. and Tabellaria flocculosa (Roth) Kütz
and the same diatoms were found in the top layer. In
the bottom layer the same species are complemented
by Cyclotella michiganiana Skvortzov 1937, C. bodanica
var. lemanica (O. Müll. ex Schröter) Bachm. and
Pseudostaurosira brevistriata (Grun.) D.M. Williams
& Round. The presence of P. brevistriata is a sign of
previously more favorable trophic conditions for algae
development. The ratios between the biogeographic
groups have not changed much over the latest 200
years. The increase of the boreal and holarctic species
may be regarded as an indirect indicator of a certain
climate change towards warming (Figure 5d); at
the same time the portion of arctic-alpine species remains
the same.
The present typical subarctic conditions are similar
to those that existed 200 years ago and no radical
changes have taken place in the lake over this period.
A certain increase of the total abundance of diatoms
in the modern sediments along with a decrease of
the species diversity was observed and it may have
resulted from climate change toward warming. The
modern conditions are also characterized by lower values
of pH and saprobity (Figure 4). The decrease in
saprobity index is a consequence of a certain shift of
the lake’s trophic status towards oligotrophy.
There have been no significant changes in ecological
conditions (Figure 5). The proportions of diatoms
tolerating different pH-values have not changed except
for some decrease of the alkalifilic diatoms. Salinity
also remained at the same level and the halophobs
and oligohalob-indifferents dominate, which
is typical of low-mineralized waters. The water level
and volume have not changed significantly as the proportions
of planktic forms have remained at the same
level. Decrease in the amount of plankto-benthic
diatoms along with increase of benthic diatoms may
be a sign of changes in the shoreline or development
of new type of bottom and substrate, which supports
the development of richer benthic communities.
124

Figure 4. Diatom complexes of Lake Shuonijaur: total abundance (N(tot), mln.ex./g),
species diversity (H’, bit/ex.), pH recontructed from diatoms (pH(diatom)) and saprobe
index (S).
Figure 5. Ecological characteristics of the diatom complexes in Lake Shuonijaur: a) pH-tolerance groups;
b) habitat groups; c) salinity groups; d) biogeographic groups.
125

Lake Harrijärvi
In Lake Harrijärvi the top layer of sediments (interval
0–1 cm) and different parts of the column (intervals
5–6, 11–12, 15–16, 18–19 and 29–30 cm), which have
formed in different periods, were analyzed. The
age of bottom sediments was 240 years. Lake Harrijarvi
is a typical subarctic lake with low salinity and an
oligotrophic status
Brachysira brebissonii Ross, B. vitrea (Grun.) Rossin
Hartley and Frustulia saxonica Rabenh form a large
proportion of the present-day periphyton. Typical
species in the top sediment layers were the same
supplemented with Cyclotella comensis Grunow in
van Heurck 1882. In the older layers the diatom complexes
also included C. kuetzingiana Thwaites and A.
alpigena. No dramatic differences were found in the
species composition for the different periods.
Total abundance (N) increases gradually from
the lower sediment layers and reaches the maximal
values in the sediment layer 15–16 cm, which is
130 years old (Figure 6). Benthic species preferring
pH

Figure 6. Diatom complexes of Lake Harrijarvi: total abundance (N(tot), mln.ex./g),
species diversity (H’, bit/ex.), pH recontructed from diatoms (pH(diatom)) and saprobe
index (S).
Figure 7. Ecological characteristics of the diatom complexes in Lake Harrijarvi: a) pH-tolerance
groups; b) habitat groups; c) salinity groups; d) biogeographic groups.
127

Lake Rabbvatnet
In Lake Rabbvatnet the diatom complexes were analyzed
in the top layer of sediments (interval 0–1 cm)
and in different parts of the column (intervals 1–1.5,
4–4.5, 6–6.5, 10–11, 20–21, 30–31, 42–43, and 43–
44 cm). The total age of the sediments is 680 years.
Lake Rabbvatnet is a typical subarctic lake with an
oligotrophic status.
Typically subarctic diatom flora dominated in the
lake phytoplankton throughout the whole study period:
arctic-alpine phytoplankton formed a significant
portion (up to 18 %) while the boreal portion was insignificant.
The typical diatoms in the top layer were
A. alpigena, P. brevistriata, Cyclotella ocellata Pant.,
C. bodanica Eulenstein. and Staurosira construens
Ehrb. In the middle part of the column the main species
were T. flocculosa, Denticula tenuis var. tenuis
Kütz., C. rossii, C. bodanica var. lemanica and C.
schumannii. In the oldest layers Fragilariforma virescens
(Ralfs) D.M.Williams & Round and C. michiganiana
were the most abundant species.
Diatom complexes are characterized by significant
changes both in the quantitative characteristics and
in the dynamics of the ecological structure. The total
abundance (N) is characterized by gradual growth as
time passes and it grows almost by 10 times (Figure
8). The primary productivity of the lake has also gradually
grown. Two maximums in abundance can be
seen: 4–6.5 cm (70–100 years old) and in the present-day
layer 0–0.5 cm. The turn of the 20 th century
was the time for the most significant changes in the
lake ecosystem and many ecological indicators are
characterized by extreme values. Presumably some
transformation of the trophic structure of the communities
occurred, which caused the maximum value of
the saprobity index in 1900 (Figure 8). The same was
also observed in Lake Harrijarvi (Figure 6).
The species diversity of diatoms has also changed:
there is a gradual increase of the Shannon-Weaver
species diversity index (H’) starting from the ancient
layers up to 10-11 cm (1830’s) where the maximum
diversity was observed. This process is probably the
due to ending of the Little Ice Age. Further warming
early in the 20 th century led to reduction of species
diversity along with increase of abundance (Figure 9).
The species diversity grows again towards present
due to climatic changes towards warming along with
industrial impact.
Reconstruction of pH from diatom complexes showed
that throughout the whole period the lake water
was characterized by near-neutral values and fluctuations
were insignificant. No fundamental changes were
detected in the main pH tolerance groups’ proportions.
In the 14 th century the lake was characterized
by more acidic conditions with pH< 7.0, which was
caused by the influence of the Little Ice Age. Closer to
the 16 th century pH grew and since then it has never
dropped below 7.0. The maximum values were characteristic
of the early 20 th century and the present.
These particular periods are also described as the
warmest. Reduction of pH in the middle of the 20 th
century is a direct consequence of deposition of acidifying
compounds from the Pechenganikel though
this reduction does not exceed natural fluctuations of
pH (Figure 8). Reduced pH allowed development of
diatoms preferring more acidic water.
Changes were also noted in the ecological structure
of the diatom complexes (Figure 9). Changes
in water salinity were insignificant: only the proportion
of indifferent diatoms increased and the halofilous
diatoms decreased. Presumably the ratio of these
groups reflects primarily the intensiveness of erosion
processes in the lake’s catchment area. The salinity
was highest in the Little Ice Age when catchment was
minimal. Throughout the whole time typical halophob
diatoms characteristic of arctic and subarctic freshwater
ecosystems were present in the lake.
Gradual reduction of planktic forms in the period
from the 14 th century through late 18 th century illustrates
reduction of the water quantity in the lake during
the Little Ice Age. At the same time the proportion of
benthic and plankto-benthic algae grows which is a
sign that the lake has become shallow. Climate warming
early in the 20 th century again caused increase
of water level and development of planktonic diatoms.
Evidently in the 1930’s the lake had more water than
today.
The present-day diatom complexes confirm the
reduction of pollution as well as the more favorable
climatic conditions for development of diatoms, which
shows in a dramatic increase of quantitative parameters.
128

Figure 8. Diatom complexes of Lake Rabbvatnet: total abundance (N(tot), mln.ex./g),
species diversity (H’, bit/ex.), pH recontructed from diatoms (pH(diatom)) and saprobe
index (S).
Figure 9. Ecological characteristics of the diatom complexes in Lake Rabbvatnet: a) pH-tolerance groups;
b) habitat groups; c) salinity groups; d) biogeographic groups.
129

Conclusions
The accumulation and distribution of elements, including
heavy metals, in the bottom sediments of lakes
were thoroughly assessed. Four aspects were reviewed:
1) background contents, 2) vertical distribution
of elements, 3) concentrations in the surface layers
of sediments, 4) determination of anthropogenic load
intensity by the factor and the degree of pollution,
caused by the heavy metals accumulated in the sediments.
It was established that the largest background concentrations
of heavy metals in bottom sediments were
noted in different lakes, which is caused by geochemical
and morphometric peculiarities of the catchment
territory and of the lake itself. Growth of Ni, Cu and
Co content in the sediments dates back to the 1920s
and 1930s and maximum values are reached in the
1970s–1980s as a result of metallurgical activities.
Emissions into the atmosphere from the Pechenganikel
are the major source of high concentrations of
Ni, Сu and Co at the distance up to 40–50 km. Similar
pattern is observed in the distribution of alkaline and
alkaline-earth metals. In the more distant lakes the
main pollution elements are the chalcophile Pb, Hg
and As, which in the latest decades have obtained the
status of global pollution elements. The area up to 50
km is the most intensively polluted.
Diatom complexes of all the lakes testify of the
changes occurring in their ecosystems over the studied
historical periods. The impact of natural processes
associated with the dynamics of the climatic
system and industrial processes associated with the
Pechenganikel have been detected. However no fundamental
changes in the ecosystems of the lakes have
been detected; they all correspond to typical subarctic
oligotrophic lakes with low salinity.
The most important climatic events in the development
of lakes were 1) the Little Ice Age (14 th –19 th
centuries) when low temperatures contributed to acidification,
reduction of water quantity and decrease
of production and 2) warming in the 20 th century, the
maximums of which fell on the period 1900s through
1930s and on the two latest decades (Figure 10). In
between these events the ecosystems of the lakes
were affected by industrial load, primarily by acidification
caused by deposition from the Pechenganikel. At
present the consequences of production decrease are
showing. It is impossible to draw a decisive conclusion
regarding the dramatic climate changes in the latest
decades; warming in the early 20 th century turned
out to be more significant for the studied lakes. At the
same time, the remaining level of industrial load evidently
impedes the analysis of the consequences of
climatic changes.
Figure 10. The diatom abundance reaction on the cooling-warming periods: 1. Lake
Rabbvatnet and 2. Lake Harrijarvi. LIA=Little Ice Age.
130

References
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364 p. (in Russian)
Barinova, S.S., Medvedeva, L.A., Anisimova, O.V. 2006: Diversity of algal indicators in environmental assessment. Pilies
Studio. Tel-Aviv. 498 p. (in Russian)
Dauvalter V.A., Kashulin N.A., Sandimirov S.S. 2012: Tendencies of alterations of the chemical composition of bottom
sediments of freshwater Subarctic and Arctic water bodies, affected by natural and anthropogenic factors. Kola Science
Center RAS. Applied Ecology of the North. Release 1. 2 (9): 54–87. (in Russian)
Davydova, N.N. 1985: Diatom algae – indicators of the natural conditions in the reservous in Holocene. Nauka. Leningrad.
244 p. (in Russian)
Denisov, D.B., Dauvalter, V.A., Kashulin, N.A., Kagan, L.Y. 2006: Long-term changes in the state of the sub-arctic waters in
the conditions of anthropogenic load (according to the diatom analysis). Biology of Inland Waters 1: 24–30. (in Russian)
Håkanson L. 1980: An ecological risk index for aquatic pollution control – a sedimentological approach. Water Research 14:
975–1001.
Håkanson L., Jansson M. 1983: Principles of lake sedimentology. Springer-Verlag. Berlin. 316 p.
Kashulin N.A., Sandimirov S.S., Dauvalter V.A., Terentjev P.M., Denisov D.B. 2009: Ecological catalogue of the lakes of the
Murmansk Region. North-western part of the Murmansk Region and of the territory of the border with neigbouring countries.
Kola Science Center RAS. Apatity. I part 226 p. II part. 262 p. (in Russian)
Krammer, K. & Lange-Bertalot, H. 1986. Bacillariophyceae, volume 1: Naviculaceae. In: Ettl, H., Gerloff, J., Heynig, H.
& Mollenhauer D. (ed.): Süsswasserflora von Mitteleuropa, Band 2. Stuttgart, Gustav Fischer Verlag, Jena. 876 p. (in
German)
Krammer, K. & Lange-Bertalot, H. 1988. Bacillariophyceae, volume 2: Bacillariaceae, Epithemiaceae, Surirellaceae. In: Ettl,
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Krammer, K. & Lange-Bertalot, H. 1991. Bacillariophyceae, volume 3: Centrales, Fragilariaceae, Eunotiaceae. In: Ettl, H.,
Gerloff, J., Heynig, H. & Mollenhauer, D. (ed.): Süsswasserflora von Mitteleuropa, Band 2. Stuttgart, Gustav Fischer
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Krammer, K. & Lange-Bertalot, H. 1991. Bacillariophyceae., vulume 4: Achnanthaceae. Kritishce Ergänzungen zu Navicula
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131

4 Biology
DMITRII DENISOV, SVETLANA VALKOVA, JUKKA YLIKÖRKKÖ
4.1 Phytoplankton
Phytoplankton are used to monitor nutrient enrichment.
Phytoplankton biomass and water chlorophyll
content are proven to correlate with concentrations
of nutrients, especially phosphorus, in lakes
(eg. Schindler 1978). Phytoplankton quality also indicates
the lake trophic level. The amount of cyanobacteria
(blue-green algae) in the assemblage is scarce
in oligotrophic lakes but tends to grow with increasing
phosphorus content, especially in relation to nitrogen
(eg. Smith 1983). Climatic factors affect plankton assemblage
directly as temperature and through various
indirect ways through nutrient flux.
Toxicity of copper to freshwater phytoplankton varies
widely depending on taxa sensitivity and specific
water chemistry. The bioavailable fraction of metals
present depends on the amount of dissolved organic
carbon, among other factors. Photosynthesis
and growth of sensitive green algae may be inhibited
roughly at >10 μg Cu/l (USEPA 2007). Algae appears
rather resilient to nickel (USEPA 1980) and at the measured
levels of nickel no growth-inhibition should be
evident.
In an earlier study in the Jarfjord and Pechenga regions
phytoplankton density was found to be the lowest
in the most acidified lake (Nøst et al. 1997).
Materials and methods
16 lakes were selected for phytoplankton sampling
and analysis in the project area (Introduction, Figure
1). Small lakes previously included in international
projects were studied, as well as new potential lakes
for the future monitoring network. On the Russian side,
attentoin was paid not only to typical small lakes
but also to relatively large water bodies (lakes Ilja-
Nautsijarvi and Ala-Nautsijarvi), which gives a better
understanding of the Pasvik River catchment area.
Phytoplankton sampling in the field followed national
standards. Samples were collected monthly during
June–September. All lakes were sampled at least in
August. Tube sampler was used to collect integrated
samples. Samples were taken from 0–2 m column in
Total of 117 taxa of algae on species or variety level
from seven systematic groups were found: Cyanophyceae
22, Chlorophyta 22, Charophyceae 19,
Chrysophyceae 5, Dinophyta 6, Bacillariophyceae 42
and Cryptophyceae 1 taxon. The number of species in
each lake is shown in Figure 1.
The highest species diversity was found in Lake
Virtuovoshjaur. This is likely because there are favo-
Finland, from 0–2 m, 2–5 m and 5–10 m columns in
Russia and from 0–10 m column in Norway. Russian
and Norwegian samples were filtered through 20 µm
plankton net. Finnish and Norwegian integrated samples
were taken from composite that represents the
average phytoplankton density of sampled depths. All
samples were preserved in Lugol’s solution.
Laboratory procedures included identification and
enumeration of phytoplankton taxa. Taxa densities
were estimated.
Floristic analysis based on similarity coefficient
(Sørensen 1948, Czekanowski 1909) was performed
to classify the lakes according to their phytoplankton
species structure. Sørensen’s similarity coefficient:
K s
= 2c / a+b
where а is number of taxa of the one site, b is number
of taxa of another site and с is number of taxa
common to both sites. Sørensen-Czekanowski’s similarity
coefficient (considering the quantitative values):
K
N
∑
i
i=
1
s
=
N N
∑
i=
1
min(A ,B )
∑
i=
1
where A i
and B i
are abundance values of species i
in lakes A and B, and N is the total number of species.
The analysis was performed with the help of software
module Graphs (Novakovsky 2004).
Results and discussion
A
i
+
i
B
i
132

Gardsjøen
Holmvatnet
Rabbvatnet
Durvatn
average
0 10 20 30 40 50 60
6
4
5
7
5,5
Lampi 222
Harrijärvi
Pitkä-Surnujärvi
Sierramjärvi
average
6
12
13
12
17
Pikkujarvi
34
20
19
Shuonijaur
Ala-Nautsijarvi
Ila-Nautsijarvi
Toartesjaur
Virtuovoshjaur
Riuttikjaure
Kochejaur
average
11
16
30
31
26,375
Jarfjord Vätsäri Russia
50
Figure 1. The number of phytoplankton taxa
in the monitored lakes.
Figure 2. Phytoplankton
communities of the
monitored lakes
133

Figure 3. Lakes’ classification by floristic analysis based on
Sørensen’s similarity coefficient.
Figure 4. Lakes’ classification by floristic analysis based on
Sørensen-Czekanowski’s similarity coefficient.
rable conditions for algal growth especially in terms
of nutrients (see Chapter 4, Water Quality). The lakes
in Jarfjord area were found to have the lowest diversity,
a difference which was not statistically significant
(see Chapter 2). The Jarfjord lakes have higher
sulphate, nickel and copper concentrations. However,
the catchment areas in Jarfjord tend to be smaller and
soil layer thinner compared to the other regions, which
also might explain the result.
The phytoplankton species composition and the
dominating taxa were variable within regions and lake
types (Figure 2). The most abundant phytoplankton
groups were diatoms (Bacillariophyceae, at most 64
%), blue-green (Cyanophyceae, 94 %), yellow-green
(Chrysophyceae, 89 %) and green algae (Chlorophyta,
97 %). Dinophytes (at most 9 %) and Charophytes
(

ure 5). One of the groups included all the Russian
lakes in the southern part of the Pasvik catchment basin;
the other group comprised the Finnish and Norwegian
lakes. Lakes Pikkujarvi and Gardsjøen were
found to be the most different. This can be explained
by the high pollution of Lake Pikkujarvi, closest to the
Pechenganikel, and also by the nutrients it receives
from agricultural lands on its shores. Lake Gardsjøen
differs from the other lakes because it has species not
found in them: for example, green-algae Raphidocelis
subcapitata and diatom Amphora ovalis. According
to the hydrochemical analysis, the lakes of Jarfjord
are the most polluted with heavy metals. Copper
and nickel concentrations in the bottom sediment are
also high. Industrial pollution and a short distance to
the sea are the possible reasons for communities with
a large proportion of green algae which is not common
in subarctic. At the same time, the most specific
plankton develops in Lake Gardsjøen, which may result
from the peculiarities of morphometry, nutrition regime
and other local factors.
Identifying groups by phytoplankton species composition
is to describe the regional characteristics and
lake conditions. The division of the lakes into groups
was primarily defined by the hydrochemical conditions.
The largest differences were found in the water
bodies exposed to industrial impact. Jarfjord lakes
deviated with higher contents of suphates, certain
metals of industrial origin and more marine chlorides.
The Russian lakes were rich in nutrients, which was
reflected in the phytoplankton composition.
Chlorophyll a content in the Vätsäri lakes were low,
all less than 2 µg/l, which indicates oligotrophic status.
Assessment of photosynthetic pigments and biomass
for the Russian lakes to evaluate their trophic status
is presented in Figure 6. The highest concentration
of chlorophyll a and the highest phytoplankton biomass
was found in Lake Pikkujarvi, which is exposed
to anthropogenic eutrophication and the trophic status
of which is eutrophic. The other lakes were classified
as oligotrophic, based on chlorophyll a and phytoplankton
biomass (Kitaev 1984). Among the oligotrophic
lakes, the highest concentrations of chlorophyll
a were found in lakes Toartesjaur and Ilja-Nautsijarvi,
which is probably associated with intensive development
of blue-green algae: Anabaena sp., Dolichospermum
lemmermannii and Chroococcus dispersus.
In favorable conditions these species may cause algal
blooms. In the same lakes carotenoid concentrations
were relatively high: this is an indirect indicator of detritus
in the water column, as well as of “aging” groups
of phytoplankton. The blue-green algae in the samples
were often associated with detritus.
In all of the study lakes, except in Lake Pikkujarvi,
the average plankton biomass during the study period
(2012–2013) did not exceed the average of the Kola
Peninsula lakes: 0.6–2.5 g/m 3 in the tundra and forest
tundra lakes and 0.56–2.96 g/m 3 in the north taiga lakes
(Letanskaya, 1974, Kupetzkaya et al. 1976).
Principal component analysis was performed in the
single factor space combining hydrochemical parameters
and certain functional characteristics of phytop-
Figure 5. Lakes classification: association
by the strongest relations based on
Sørensen similarity coefficient.
135

Figure 6. Photosynthetic pigment content, total biomass and trophic state of the phytoplankton (Russian lakes): a) photosynthetic
pigments (mg/m 3 ), b) biomass (g/m 3 ), c) photosynthetic pigments (mg/m 3 ) excluding Lake Pikkujarvi, d) biomass (g/m 3 ) excluding
Lake Pikkujarvi
lankton to assess the factors of phytoplankton development
conditions. Nutrients, in particular nitrates
and phosphates, do not seem play an important role
in phytoplankton development in the study lakes, i.e.
a large part of the assemblages are consistent with
oligotrophic status and there are species that do not
require large amounts of nutrition.
Salinity and water color were found to be the most
important hydrochemical factors that have a positive
impact on phytoplankton development. This is confirmed
by positive relation of chlorophyll a and plankton
biomass. Possibly, in the conditions of relatively low
salinity, mineral components and humic acids coming
from the catchment area become important for the
plankton. The results indicate that organic compounds
in water are associated with plankton vitality. There
is a positive relation between phytoplankton biomass
and copper and nickel, which confirms the absence
of their negative impact; in the current concentrations
these elements only contribute to general salinity.
Similar groups in terms of phytoplankton communities
and hydrochemical indicators were identified.
Lake Pikkujarvi differed most from the other lakes:
specific hydrochemical conditions are formed there
because of eutrophication and pollution resulting
from the proximity of the Pechenganikel. This has an
impact on phytoplankton development characterized
by the largest quantitative parameters (biomass and
chlorophyll a concentration) among the studied lakes.
Lake Pikkujarvi is the most transformed by human activities,
which makes it an interesting lake for the integrated
environmental monitoring network.
Also Lake Toartesjaur was different from the other
lakes: the phytoplankton communities are characterized
by high concentration of chlorophyll b. Lake Toartesjaur
has a higher concentration of zinc than the
other lakes which may have had a stimulating effect
on development of algae containing this pigment.
The other lakes reflect background conditions of
water quality and plankton assemblages. In this group
the most accessible lakes, Shuonijaur and Vuirtuovoshjaur,
may be selected for future monitoring. It
would be reasonable to sample phytoplankton in the
other lakes less frequently.
136

Conclusions
The phytoplankton assemblages in the project area
lakes were found to be variable, showing no regional
concordance. In general the Russian lakes, which were
larger and more southern, had the highest species
diversity and the Jarfjord lakes the lowest.
Diatoms, blue-green, green, and yellow-green algae
formed the most abundant algal groups. Green
algae were found to increase from south to north and
to be the highest in the lakes of Jarfjord, as well as in
lakes Pikkujarvi (Russia) and Sierramjärvi (Finland).
The Pechenganikel seems to have an impact on the
species composition, as the results are consistent
with the data on Lake Kuetsjarvi, which is exposed to
direct pollution with copper and nickel production process
discharge, and where the portion of green algae
is also large.
The floristic analysis divided the lakes into three
groups, mainly according to geographic territorial areas.
Lakes Pikkujarvi and Harrijärvi, as well as Lake
Toartesjaur, were found to be the most different from
the others, because of specific local conditions and
hydrochemical parameters. The largest differences
were found in the lakes under anthropogenic impact,
which is to some extent consistent with the hydrochemical
data: the Russian lakes are rich in nutrients and
the lakes of Jarfjord are dramatically different because
of high concentration of marine ions as well as sulfates
and metals originating from the Pechanganikel.
The lakes also have different-sized catchments: generally
the largest in Russia and smallest in Jarfjord.
Among the Russian lakes Lake Pikkujarvi is notably
higher in phytoplankton biomass and photosynthetic
pigments because of anthropogenic eutrophication.
The other lakes are regarded as oligotrophic according
to their chlorophyll a concentration and phytoplankton
biomass, which both represent typical levels
for the region.
The principal component analysis of the Russian
lakes established that water salinity and color are the
most important hydrochemical factors having a positive
impact on phytoplankton development. Mineral
components and humic acids are important for the algae
in the conditions of relatively low salinity. No significant
impact by contaminants, copper and nickel in
particular, on phytoplankton quantitative parameters
was found. Lake Pikkujarvi exposed to eutrophication
and pollution was found to be the most different from
the other lakes.
References
Novakovsky, A.B. 2004: Capacity and operational principles of the software module Graphs. Automation of scientific research.
Komi Science Center UrO RAS. Syktyvkar. 27 p. (in Russian)
Czekanowski, J. 1909: Zur differential Diagnose der Neandertalgruppe. Korrespindenz-Blatt der Deutschen Gesellschaft für
Anthropologie, Ethnologie und Urgeschichte 40: 44–47. (in German)
Kupetzkaya, G.K., Velikoretzkaya, I.I., Zenus, B.G. et al. 1976: Large lakes of the Kola Peninsula. Nauka .349 p. (in Russian)
Letanskaya G. I. 1974: Phytoplankton and primary production of the lakes of the Kola Peninsula. In: Lakes of the differentlandscapes
of the Kola Peninsula. T.2: 78–19. Nauka. Leningrad. (in Russian)
Nøst, T., Anatoli, L., Schartau, A.K., Kashulin, N., Berger, H.M., Yakolev, V., Sharov, A., Dauvalter, V. 1997: Impacts of pollution
on freshwater communities in the border region between Russia and Norway III. Results of the 1990-96 monitoring
programme. NINA Norsk institutt for naturforskning. 37 p.
Schindler, D.W. 1978: Factors regulating phytoplankton production and standing crop in the world’s freshwaters. Limnology
and Oceanography 23(3): 478–486.
Smith, V.H. 1983: Low nitrogen to phosphorus ratios favor dominance by blue-green algae in lake phytoplankton. Science
221(4611): 669–671.
Sørensen T. A. 1948: Method of establishing groups of equal amplitude in plant sociology based on similarity of species
content. Kongelige Danske Videnskabernes Selskab. Biologiske Skrifter 5(4): 1–34.
USEPA. 1980. Ambient water quality criteria for copper. U.S. Environmental Protection Agency. 84 p.
USEPA. 1986. Ambient aquatic life water quality criteria for nickel. U.S. Environmental Protection Agency. 93 p.
137

4.2 Periphyton
Epilithic diatoms on stone substrate are studied here
as periphyton. In terms of chemical factors, the diatom
communities are primarily a result of water trophic state,
organic matter and acidity. The growth substrate
quality affects the periphyton especially in lake habitats.
Diatom communities are used in assessing several
environment variables.
According to Soininen et al. (2004) phosphorus
content is one of the major environmental factor affecting
diatom communities. The lakes studied in the project
are oligo- or mesotrophic. As all biota in nutrientpoor
habitat, periphyton can be expected to react to
even small changes in available mineral nutrients.
Diatom communities in water courses have been
observed to react to nutrient increase by increasing
numbers of eutrophy-associated species, such as those
in Nitzschia-genus (Eloranta et al. 2007). Epilithic
diatoms take in all nutrients from the water and therefore
express the prevailing nutrient status well.
Traditionally diatom communities are also used in
assessing the organic pollution (the saprobic level)
(e.g. Sladecek 1973).
Similar to other biota, acidity generally decreases
diatom species richness: sensitive species disappear
and few tolerant species increase (Patrick 1977).
Diatom taxa can be categorized by their tolerance to
acidity (Van Dam et al. 2004). However, many species
tolerate changes in pH rather well and acidofil taxa is
found also in neutral water, but in less extent compared
to acidified waters (Eloranta 1990).
There is some experimental data regarding diatom
responses to heavy metals. According to USEPA
(2007) freshwater Nitzschia palea growth is inhibited
at copper concentration of 5 µg/l. This gives a rough
reference to possible pollution-related effects in the
most sensitive diatom taxa. There is less accurate information
about nickel compounds’ toxicity to diatoms.
Patrick et al. (1975) reported nickel to cause dominance
of green and blue-green algae over diatoms.
Materials and methods
Samples were collected by scraping rocks (roughly
fist-sized) from c. 20 cm depth in littoral zone of the
lakes. Detached periphyton was collected and preserved
in ethanol in Finland, formaldehyde in Norway
and Lugol’s solution in Russia. In Finland samples
were collected from 5 stones in 2–3 different littoral
stations in September; in Russia and Norway 10 rocks
from one location in August 2013.
Diatom slides for the microscope analyses were
prepared using standard traditional methods (Zhuze
et al. 1949, Davydova 1985, Denisov 2007). The organic
matter was eliminated by hydrogen peroxide,
then diatom valves were separated from other mineral
components by the difference in sedimentation velocity.
All diatoms have been identified, if possible, to
intraspecific taxa (Krammer & Lange-Bertalot 1986-
1991). Diatom taxa identification and enumeration followed
principles in standard EN 14407:2004.
A nutrient status analysis was done using the
Diatom Assessment of Lake Ecological Status (DA-
LES), which is based on average score per taxon method
(UKTAG 2008).
The diatom-inferred value of the pH has been calculated
according to method of Moiseenko & Razumovsky
(2009) with the following formula:
pH = Σphi⋅k / Σk,
where phi – individual numeric value of the each taxaindicator;
k – abundance value (quantity).
Periphyton groups were determined according to
their preferred pH: neutrophils with pH-optimum at
pH 7.0, circumneutrophils with a range of pH close to
neutral, indifferent capable of developing at a relatively
wide range of pH, alkaliphils preferring pH > 7.0,
alkalibionts preferring pH 7.6 and above, acidophiles
preferring pH

Results and discussion
Sampled lakes had diatom communities typical of
nutrient-poor north boreal lakes. There were differences
in the number of species and species composition
between the areas (Figure 9). The diatom communities
reflected the lakes’ humic and nutrient content.
DALES indicated low nutrient status for all and saprobic
index oligosaprobia for most of the lakes (Table
3). Tabellaria flocculosa is considered acidophilous,
mainly occurring at pH less than 7 (Van Dam et al.
1994). It is a common species in the research areas,
which are naturally low alkaline. pH estimated through
diatom communities was quite neutral and corresponded
to the measured pH values.
Vätsäri area had the highest species diversity: on
average 59 littoral diatom species. The lakes were
dominated mainly by Brachysira vitraea, Tabellaria
flocculosa and Frustulia rhomboides. These taxa are
associated with low nutrient concentrations (UKTAG
2008, Kelly et al. 2001), and also considered sensitive
to organic pollution (Cemagref 1982). Diatom communities
were oligosaprobic.
Gardsjøen
Holmvatnet
Rabbvatnet
Durvatn
Lampi 222
Harrijärvi
Pitkä-Surnujärvi
Sierramjärvi
Shuonijaur
Ala-Nautsijarvi
Toartesjaur
Virtuovoshjaur
Riuttikjaure
23
22
31
34
22
30
50.7
37.5
46
46
56
Figure 9. The number of periphytic diatom species in each lake.
Vätsäri values are averages from 3 sub-samples.
67
83.3
0 20 40 60 80 100
Jarfjord Vätsäri Russia
Table 3. DALES index values and their respective ecological quality ratios (EQR) (all values represent high status class EQR ≥
0.8), the diatom-inferred saprobe index (S) and saprobe zone and the diatom-inferred and measured pH of the investigated lakes.
DALES
Class
(EQR)
S Saprobe zone pH (diatom) pH (measured)
Vätsäri
Lampi 222 14 high (>1) 1.16 α-oligosaprobic 6.8 6.7
Harrijärvi 6 high (>1) 1.25 α-oligosaprobic 6.7 6.7
Pitkä-Surnujärvi 20 high (1.0) 1.22 α-oligosaprobic 6.9 6.7
Sierramjärvi 26 high (0.9) 1.44 α-oligosaprobic 7.0 6.8
Russia
Shuonijaur 26 high (0.9) 1.63 β-mesosaprobic 6.8 6.8
Ala-Nautsijarvi 27 high (0.9) 1.57 α-oligosaprobic 7.3 7.0
Toartesjaur 28 high (0.9) 1.46 α-oligosaprobic 7.0 6.9
Virtuovoshjaur 21 high (1.0) 1.39 α-oligosaprobic 7.0 6.9
Riuttikjaure 36 high (0.8) 1.42 α-oligosaprobic 7.2 7.1
Jarfjord
Gardsjøen 20 high (1.0) 1.55 α-oligosaprobic 6.9 6.8
Holmvatnet 12 high (>1) 1.34 α-oligosaprobic 6.9 6.8
Rabbvatnet 19 high (>1) 1.45 α-oligosaprobic 7.0 7.0
Durvatn 23 high (0.9) 1.22 α-oligosaprobic 7.0 7.0
139

In the southern lakes in Russia, there were, on average,
40 species in a lake. Tabellaria flocculosa was
also dominating in these lakes. Taxa of slightly higher
nutrient level is more common there, such as Denticula
tenuis in Ala-Nautsijarvi. In Riuttikjaure the dominating
species was Epithemia sorex, which is considered
taxa of moderate nutrient level (UKTAG 2008) and
tolerant to mild organic pollution (Cemagref 1982).
Saprobic index indicated oligosaprobia for the lakes,
excluding Shuonijaur that was on mesosaprobic level.
The number of diatom species in Shuonijaur was low,
22 species, which was similar to Jarfjord. Shuonijaur
copper concentration has, at times, exceeded the potentially
toxic levels to diatom growth.
Jarfjord lakes had the lowest species diversity: on
average 27 species. The difference in species diversity
in relation to Vätsäri is statistically significant (see
Chapter 2). Tabellaria flocculosa or similarly oligotrophic
Brachysira styriaca dominated in Jarfjord lakes.
All the lakes were oligosaprobic by saprobic index.
The Jarfjord lakes all had elevated sulphate levels
(see Chapter 4, Water quality). This indicates that the
lakes might have experienced some degree of acidification
in the past. As a result diatom community would
have suffered a loss of species. Moreover, all the lakes
had copper and nickel concentrations higher than
in the other regions. Biological impact is the combined
effect of both metal pollution and acid deposition.
It should be noted that catchment structure or soil
quality was not controlled in the study. Sampling differences,
catchment size and soil quality and thickness
may also explain species diversity. In addition,
the Jarfjord lakes are slightly more north compared to
Vätsäri, which could also contribute to the result.
Conclusions
There were fewer species in Jarfjord lakes compared
to the other two regions. The main chemical differences
to other lakes are elevated levels of sulphate and
heavy metals. Chemical quality in Jarfjord does not
imply acidification, but heavy metal pollution and past
changes in alkalinity may explain the difference. Similarly
Lake Shuonijaur in the vicinity of Nikel has notably
low diatom species diversity and elevated levels of
copper and nickel.
Periphytic diatoms are quick and straightforward to
sample. It is found to be a relatively effective monitoring
target for the current environmental impacts and
therefore recommended for future monitoring.
References
Barinova, S.S., Medvedeva, L.A., Anisimova, O.V. 2006: Diversity of algal indicators in environmental assessment. Pilies
Studio. Tel-Aviv. 498 p. (in Russian)
Cemagref 1982: Etude des méthodes biologiques d´appréciation quantitative de la qualité des eaux., Rapport Q.E. Lyon-
A.F.Bassion Rhône-Méditeranée-Corse: 218 p. (in French)
Davydova, N.N. 1985: Diatom algae – indicators of the natural conditions in the reservous in Holocene. Nauka. Leningrad.
244 p. (in Russian)
Denisov, D. 2007: Changes in the hydrochemical composition and diatomic flora of bottom sediments in the zone of influence
of metal mining production (Kola Peninsula). Water Resources 34(6): 682-692.
Gaiser, E. 2010: Diatoms as indicators in wetlands and peatlands. In Gaiser, E., Rühland, K.: The Diatoms: Applications for
the Environmental and Earth Sciences. 2 nd ed. Cambridge: Cambridge University Press, p. 473-496.
Eloranta, P. 1990: Periphytic diatoms in the acidification project lakes. In Kauppi, P., Anttila, P. & Kenttämies, K. (eds.).
Acidification in Finland. Springer Verlag. Berlin. 1237 p.
Eloranta, P., Karjalainen, S.M., Vuori, K-M. 2007: Piileväyhteisöt jokivesien ekologisen tilan luokittelussa ja seurannassa:
menetelmäohjeet. 58 p. (in Finnish with an abstract in English)
Kelly, M.G., Adams, C., Graves, A.C., Jamieson, J., Krokowski, J., Lycett, E.B., Murray-Bligh, J., Pritchard, S., Wilkins, C.
2001: The trophic diatom index: a user’s manual. Revised edition. Research & Development, Technical report E2/TR2.
135 p. http://botany.natur.cuni.cz/neustupa/trophic-diatom-index.pdf [accessed 6.3.2014]
Krammer, K. & Lange-Bertalot, H. 1986. Bacillariophyceae, volume 1: Naviculaceae. In: Ettl, H., Gerloff, J., Heynig, H.
& Mollenhauer D. (ed.). Süsswasserflora von Mitteleuropa, Band 2. Stuttgart, Gustav Fischer Verlag, Jena. 876 p. (in
German)
Krammer, K. & Lange-Bertalot, H. 1988. Bacillariophyceae, volume 2: Bacillariaceae, Epithemiaceae, Surirellaceae. In: Ettl,
140

H., Gerloff J., Heynig, H. & Mollenhauer, D. (ed.). Süsswasserflora von Mitteleuropa, Band 2. Stuttgart, Gustav Fischer
Verlag, Jena. 596 p. (in German)
Krammer, K. & Lange-Bertalot, H. 1991. Bacillariophyceae, volume 3: Centrales, Fragilariaceae, Eunotiaceae. In: Ettl, H.,
Gerloff, J., Heynig, H. & Mollenhauer, D. (ed.), Süsswasserflora von Mitteleuropa, Band 2. Stuttgart, Gustav Fischer
Verlag, Jena. 576 p. (in German)
Krammer, K. & Lange-Bertalot, H. 1991. Bacillariophyceae, volume 4: Achnanthaceae. Kritishce Ergänzungen zu Navicula
(Lineolatae) und Gomphonema. In: Ettl, H., Gärtner, G., Gerloff, J., Heynig, H. & Mollenhauer, D. (ed.), Süsswasserflora
von Mitteleuropa, Band 2. Stuttgart, Gustav Fischer Verlag, Jena. 437 p. (in German)
Moiseenko T.I., Razumovskii, L.V. 2009: The New Methodic of Reconstruction Cation–Anion Balance in Lakes by Diatom
Analysis. Achieving Academy of Sciences. 427(1): 132-135. (in Russian)
Pantle, R., Buck H. 1955: Die biologische Uberwachung der Gewasser und die Darstellung der Ergebnisse. Gas- und Wasserfach.
Wasser und Abwasser 96: 609-620. (in German)
Patrick, R. 1977: Ecology of freshwater diatoms and diatom communities. In Werner, D. (ed.): Biology of diatoms. University
of California Press. Berkeley. p. 284–332.
Patrick, R., Bott, T., Larson, R. 1975: The role of trace elements in management of nuisance growths. U.S. Environmental
Protection Agency. 248 p.
Sladecek, V. 1973: System of water quality from biological point of view. Archiv für Hydrobiologie – Beiheft Ergebnisse der
Limnologie 7: 1-128.
Soininen, J., Paavola, R., Muotka, T. 2004: Benthic diatom communities in boreal streams: community structure in relation
to environmental and spatial gradients. Ecography 27(3): 330-342.
USEPA. 1986: Ambient aquatic life water quality criteria for nickel. U.S. Environmental Protection Agency. 93 p.
USEPA. 1980: Ambient water quality criteria for copper. U.S. Environmental Protection Agency. 84 p.
Van Dam, H., Mertens, A., Sinkeldam, J. 1994: A coded checklist and ecological indicator values of freshwater diatoms from
the Netherlands. Netherlands Journal of Aquatic Ecology 28(1): 117–133.
Zhuze, A.P., Proshkina-Lavrenko, A.I., Sheshukova, V.S. 1949: Diatom analyses, volume 1. Leningrad. 240 p. (in Russian)
Zhuze, A.P., Kiselev, A.I., Poretsky, V.S., Proshkina-Lavrenko, A.I., Sheshukova, V.S 1949: Diatom analyses, volume 2.
Leningrad. 238 p. (in Russian)
Photo: Helén Andersen
141

4.3 Zoobenthos
Zoobenthos, or benthic macroinvertebrates, were
studied in littoral and profundal zones. Littoral zoobenthos
responds to multiple environmental pressures
due its community’s diverse feeding adaptations
and life cycles. North boreal streams are much studied
and they have been reported to sustain lower
species richness and abundance relative to south
boreal region (eg. Sandin & Johnson 2000). This is
expected to be true also for lake habitats. Indicator
taxa for eutrophication, organic pollution (saproby)
and acidification may be recognized in zoobenthos
communities. Environmental changes are reflected to
species diversity, abundance and community composition.
The profundal zoobenthos is most of all prone
to oxygen depletion, which is similarly studied through
community metrics.
The effect of sulphate on invertebrate biota depends
on the compound it is bound to. Water alkalinity
and chloride content alleviate sulphate toxicity (Meays
& Nordin 2013). The level of sulphate measured in
project lakes is not high enough to necessarily cause
direct effects in zoobenthos but it may indirectly alter
the communities through acidification.
Invertebrate sensitivity to heavy metals is specific
to each group and life-stage. The nickel and copper
concentrations that can directly affect invertebrate viability
are higher than what was measured in the study
lakes (USEPA 2007, 1980). The bioavailable fraction
of a metal in nature depends primarily on the dissolved
organic carbon content. In a previous study in the
project area (Nøst et al. 1997) sensitive Ephemeroptera,
Trichoptera and Plecoptera species were fewer
or altogether missing in the most acidified and heavily
polluted lakes (Nøst et al. 1997).
Materials and methods
Littoral zoobenthos was collected using a kick-net on
rocky bottom substrate ca. 20–40 cm deep. Kick-net
samples were sieved with 0.5 mm mesh. Each lake
was sampled once between August and October in
2012 and 2013. Sampling procedures are summarized
in Table 5. The Finnish method is described in
detail in Meissner et al. (2013, in Finnish) and Russian
method in state standard (GOST 17.1.3.07-82). Norwegian
sampling time is 20 seconds per 1 m and the
net is emptied after 1 min sampling time. Total kicking
time is 3 min, during which a total of 9 m is moved.
Profundal zoobenthos was collected using a standard
Ekman grab from the deepest basin in each lake.
Samples were sieved with 0.5 mm mesh. Profundal
sampling was conducted at the same time with littoral
sampling. Sampling procedures are summarized
in Table 6.
Table 5. Littoral zoobenthos sampling month and the number of sampled stations and
replicates.
Month Sampling time (s.) Stations Replicates
Finland September-October 20 3* 2
Russia August 10-15 3 1
Norway September-November 20 3 2
* Harrijärvi has 2 stations and 3 replicates.
Table 6. Profundal zoobenthos sampling time and the
number of replicate Ekman grab samples.
Month
Replicates
Finland September-October 6
Russia August 2
Norway August 6
142

Results and discussion
Littoral community
The observed littoral macrozoobenthos represented
typical taxa for nutrient-poor, clear-watered lakes in
cold climate.
In the Russian lakes there were altogether 34 species.
In Vätsäri and Jarfjord lakes were total of 36 and
23 species, respectively. Chironomid species were
the most numerous in all of the regions. The number
of all the recorded families was consistently lower in
Jarfjord (Figure 10). The difference between Jarfjord
lakes’ number of families in contrast to the other two
regions was found statistically significant (See Chapter
2).
0 5 10 15 20
Jarfjord
Gardsjøen
Holmvatnet
Rabbvatnet
0
1
3
4
5
8
lighter = total number
of families
darker = EPT families
Durvatn
4
9
Lampi 222
2
11
Vätsäri
Harrijärvi
Pitkä-Surnujärvi
4
5
10
12
Sierramjärvi
6
16
Shuonijaur
2
8
Ala-Nautsijarvi
3
11
Russia
Toartesjaur
Virtuovoshjaur
Riuttikjaure
Kochejaur
5
5
6
9
10
10
13
18
Figure 10. The number of littoral zoobenthic
families (lighter color) and the number Ephemeroptera,
Plecoptera and Trichoptera families
(darker color) in each lake and region.
Table 7. The number of species, Woodiwiss biotic index and Shannon diversity index values for the Russian,
Vätsäri and Jarfjord lake’s littoral zoobenthos communities. The consequent ecological state is assessed by
the Russian state standard limit values.
Lake Number of species Woodiwiss index Shannon index Ecological state
Russia
Virtuovoshjaur 22 10 3.81 clean
Kochejaur 25 10 4.01 clean
Ilja-Nautsijarvi 19 8 3.55 clean
Shuonijaur 12 7 2.54 clean
Ala-Nautsijarvi 14 8 3.34 clean
Toartesjaur 13 8 3.21 clean
Pikkujärvi 17 7 3.31 clean
Riuttikjaure 16 8 3.40 clean
Vätsäri
Pitkä-Surnujärvi 24 9 2.61 clean
Sierramjärvi 23 9 2.64 clean
Harrijärvi 17 9 2.84 clean
Lampi 222 13 9 2.86 clean
Jarfjord
Holmvatn 3 4 1.50 polluted
Durvatn 10 6 2.04 moderately polluted
Gardsjøen 7 3 0.60 dirty
Rabbvatn 14 8 2.16 clean
143

The Ephemeroptera, Plecoptera and Trichoptera
(so called ‘EPT’) families are considered pollutionsensitive
and therefore specifically the number of
their families is used as an indicator of environmental
status. The EPT families were again fewer in Jarfjord
lakes, but the difference was not significant at 95 %
confidence level.
Analysis of ecological state using Woodiwiss biotic
index for organic pollution and Shannon diversity
index against the Russian state limit values (GOST
17.1.3.07-82) is summarized in Table 7. All the Russian
and Vätsäri littoral zoobenthos communities yield
clean water status. Three Jarfjord lakes classify as
polluted at some degree. The three polluted lakes have
notably few taxa of zoobenthos. No chemical factor
separates the three from other lakes in Jarfjord.
It is assumable that the combined effect of industrial
pollution, namely sulphate, copper and nickel, play
a role in shaping the Jarfjord littoral zoobenthic communities
and lower their diversity. In comparison to
Vätsäri the lakes have the same trophic state and thus
should not express significant differences. However,
the latitude and catchment characteristics were not
controlled in the study: the Jarfjord lakes are generally
more north and tend to have smaller catchments
with thinner soils, both of which could have negative
impact on diversity.
Profundal community
Profundal macrozoobenthos densities and biomasses
were found to be low. In the Russian lakes there was
high variance from no zoobenthos (Virtuovoshjaur,
Kochejaur) to 1938 individuals/m 2 (Toartesjaur) (Table
8). The lakes in Vätsäri and Jarfjord had lower densities
of zoobenthos: 0–329 ind./m 2 and 52–456 ind./
m 2 , respectively (Table 8). The cause of empty samples
is probably the naturally sparse distribution of the
individuals. Chironomidae and Oligochaeta comprise
majority of the lake profundal taxa.
The Russian profundal zoobenthos communities
mainly indicate oligotrophy or mesotrophy on Kitaev’s
(2007) trophic scale based on zoobenthos biomass.
Lake Toartesjaur classified as eutrophic because more
than 70 % of the community was composed of eutrophy
indicator chironomid Limnochironomus gr. tritomus.
Vätsäri communities indicate oligotrophy. Jarfjord
communities indicate oligotrophy, apart from Lake
Durvatn, where the benthos made up mostly by Mic-
Table 8. The mean values of number and biomass, Shannon diversity index values, trophic states according to Kitaev (2007) and
status of the community based on Finnish national zoobenthos metrics (PMA, PICM) for the Russian, Vätsäri and Jarfjord lake’s
profundal zoobenthos communities. Some lakes could not be classified due to low abundance of invertebrates.
Lake
Mean values of
number (ind./m 2 )
Mean values of
biomass (g/m 2 )
Shannon
index
Trophic state
Community status
(PMA, PICM)
Russia
Virtuovoshjaur - - - - -
Kochejaur - - - - -
Ilja-Nautsijarvi 190.1 1.0 1.87 oligotrophy -
Shuonijaur 576.7 2.9 2.76 mesotrophy high
Ala-Nautsijarvi 415.2 2.1 2.42 oligotrophy high
Toartesjaur 1937.6 9.7 1.12 eutrophy high
Pikkujärvi 622.8 3.1 3.08 mesotrophy -
Riuttikjaure 692.0 3.5 2.50 mesotrophy high
Vätsäri
Pitkä-Surnujärvi 155.7 1.3 2.60 oligotrophy good
Sierramjärvi 328.7 2.7 1.50 oligotrophy high
Harrijärvi 196.1 1.6 3.00 oligotrophy good
Lampi 222 - - - - -
Jarfjord
Holmvatn 103.8 0.9 3.09 oligotrophy -
Durvatn 455.6 3.8 1.93 mesotrophy -
Gardsjøen 184.5 1.5 1.92 oligotrophy high
Rabbvatn 51.9 0.4 1.89 oligotrophy high
144

asema sp. (Trichoptera) and mussel Pisidium (Euglesa)
sp.
The Finnish community metrics faced difficulties
with low abundance of invertebrates. Some lakes had
so few taxa that PMA (Percent Model Affinity) would
have been 0, which is not a useful result. Profundal Invertebrate
Community Metric (PICM) requires certain
indicator Chironomidae and Oligochaeta on species
level to assess benthos status. Lakes that were possible
to classify, gained at least good status class. The
presence of relatively sensitive Sergentia and Cladotanytarsus
resulted in mostly high status class for
PICM. PMA gave variable results from poor to high.
The index reacted to low invertebrate density, which
is considered natural in the nutrient-poor, cold lakes.
Final profundal status was classified as good or high.
Dendrogram analysis
In relatively deep water bodies (e.g. Shuonijaur, 15-
20 m) the psychrophilic, oligo-mesotrophic larvae of
Sergentia coracina (Chironominae) dominate in the
structure of the profundal communities and cold water
oligotrophic Arctopelopia sp. (Tanypodinae) dominate
in the littoral area. Chironomidae Procladius choreus
gr. (Tanypodinae), widely spread in the Palearctic, are
encountered everywhere. The portion of Cricotopus
silvestris gr. (Orthocladiinae) is increased in shallow,
quickly warming water bodies with well developed
aquatic vegetation and periphyton cover. Increase of
anthropogenic impact leads to the reduction of relative
density of oligotrophic species and to the increase
of eurybiontic larvae of Chironomus spp.
Four isolated groups were distinguished by similarity
in the profundal macrozoobenthos. Eutrophic
Lake Toartesjaur is separate from the others, as is
Lake Riuttikjaure, where Oligochaeta dominate in
the benthos fauna. Lakes Virtuovoshjaur and Kochejaur,
characterized by the lowest diversity indicators
and zoobenthos density, form a group of their own. A
separate cluster unites lakes Gardsjøen, Shuonijaur,
Sierramjärvi and Durvatn: the common feature is the
domination of one group of invertebrates in the profundal
zoobenthos composition: for example, larvae
of Micrasema genus (Trichoptera) dominate in Lake
Durvatn whereas in Lake Shuonijaur Chironomidae
were the most abundant.
For the littoral macrozoobenthos the clusters were
somewhat different (Figure 11). Lakes Harrijarvi, Sierramjarvi
and Pitkä-Surnujärvi, where various species
of Chironomidae dominate in the macrozoobenthos,
form one cluster and Holmvatn and Durvatn other
cluster. Other water bodies are positioned on the dendrogram
in the order of increasing biodiversity of littoral
macrozoobenthos.
Figure 11. The dendrogram of the similarity of the macrozoobenthos of the studied lakes. (littoral
benthic communities, method: Single Linkage, Euclidean distance).
145

Conclusions
The results from macrozoobenthos littoral communities
indicate mostly natural ecological status with
sensitive taxa present in Russia and Vätsäri, Finland.
However, certain lakes in Jarfjord indicate pollution of
moderate or high degree. In addition there were differences
in biodiversity between the areas so that Jarfjord
lakes expressed consistently the lowest diversity.
The profundal communities had low density of individuals
but the community indices gave normal results
when there were zoobenthos in the samples.
Littoral zoobenthos is recommended for future monitoring
due to long history of monitoring and feasibility
as pollution indicator. Profundal benthic community
assessment was troubled by empty samples and naturally
very low densities and therefore profundal monitoring
should not be placed high priority in the future.
References
Kitaev, S. P. 2007: Foundations of Limnology for Hydrobiology and Ichthyology. Karelian Research Center RAS. Petrozavodsk. 395
p. (in Russian)
Meays, C., Nordin, R. 2013: Ambient Water Quality Guidelines For Sulphate. Technical Appendix. British Columbia Ministry
of Environment. 55p.
Nøst, T., Anatoli, L., Schartau, A.K., Kashulin, N., Berger, H.M., Yakolev, V., Sharov, A., Dauvalter, V. 1997: Impacts of pollution
on freshwater communities in the border region between Russia and Norway III. Results of the 1990-96 monitoring
programme. NINA Norsk institutt for naturforskning. 37p.
Sandin, L., Johnson, R.K. 2000: Ecoregions and benthic macroinvertebrate assemblages of Swedish streams. Journal of
the North American Benthological Society 19(3):462-474.
USEPA 1980: Ambient aquatic life water quality criteria for nickel. U.S. Environmental Protection Agency. 93 p. http://water.
epa.gov/scitech/swguidance/standards/criteria/upload/AWQC-for-Nickel_1986.pdf.
USEPA 2007: Aquatic life ambient freshwater quality criteria - copper. 2007 revision. U.S. Environmental Protection Agency.
204 p. http://water.epa.gov/scitech/swguidance/standards/criteria/aqlife/copper/upload/2009_04_27_criteria_copper_2007_criteria-full.pdf
Kick-net sampling of zoobenthos. Photo: Jukka
Ylikörkkö
146

4.4 Fish
PETR TERENTJEV, HELÉN ANDERSEN, GUTTORM CHRISTENSEN, GEIR DAHL-HANSEN, KATJA MÄÄTTÄNEN,
MARTTI RASK, JUKKA RUUHIJÄRVI, SAMULI SAIRANEN, ERNO SALONEN, ARI SAVIKKO
Fish communities of small border area lakes in Finland,
Russia and Norway were studied in 2013 and
2014 and the ecological status was evaluated based
on fish community variables. The final aim was to
evaluate the usefulness of fish community variables
(community structure, growth rate, maturation age,
longevity) in assessment of impacts of climate change
and hazardous substance deposition and possible
effects of acidification on fish communities of survey
lakes.
There are several possible methods for collecting
information for doing a classification depending on type
of the river or lake system. The main methods for
lakes are interviews with locals, historical data, gillnet
fishing, mapping by echo sounding or electrofishing.
The lakes in the border region all potentially influenced
by acidification. Gillnet fishing was carried out according
EU standards in order to evaluate the status
of the fish populations in the lakes
Finnish lakes Harrijärvi and Pitkä Surnujärvi were
test fished in summer 2013. Russian lakes Shuonijaur,
Ilja-Nautsijarvi, Virtuovoshjaur, Riuttikjaure and
Toartesjaur were also test fished in summer 2013 but
also additional, older data from previous years was
used in the case of some lakes to determine changes
in fish community structure. Also some results
from Lake Kochejaur, which has been a subject of fish
community research for decades, are included here.
The Norwegian lakes Durvatn, Gardsjøen, Holmvatn
and Rabbvatn were test fished in August 2013 and
Rundvatn in August 2014.
Materials and methods
Test fishing was carried out with using a stratified random
sampling method in the littoral, sub-littoral and
profundal zones (Kurkilahti 1999, CEN 2005). The
sampling was carried out by using standard NOR-
DIC multimesh gillnets (30 x 1.5 m) with 12 different
mesh size between 5–55 mm (mesh sizes 5, 6.25, 8,
10, 12.5, 15.5, 19.5, 24, 29, 35, 43 and 55 mm). The
NORDIC multimesh gillnet is the standard method in
the EU Water Framework Directive. The number of
gillnets that are recommended according to EU standards
in the survey depends on size and depth of the
lake (Table 1). The lake size varied from 0.4 km 2 to 8.5
km 2 and the depth from 10 to 25 meters which lead to
different numbers of gillnets used (Table 2).
The nets were set in the evening and hauled the
next morning after a catching period of 12–15 hours.
All lakes were sampled during one night. The catch of
each net was handled separately by mesh size and
sorted by species, counted and weighted.
Total catches, catches of species groups and
catches of each fish species were calculated as catch
per unit effort (CPUE, g/net and CPUE, number/net).
The catch of each net was handled separately and
by mesh size. Each catch was sorted by species and
then counted and weighed. For size distributions, the
total length of every fish was measured at 1 cm accuracy
in Finland and Russia and in Norway the fork
length was measured at 1 mm accuracy. Also the total
number and weight of potentially piscivorous perch
(Perca fluviatilis) (≥ 15 cm) was calculated separately
for the proportion of predatory fishes.
Hectares I II III IV
< 20 6 10 16 24
21–50 10 16 25 37
51–100 15 21 30 42
101–250 20 26 35 47
251–500 24 30 39 51
501–1000 28 36 48 64
> 1000 32 40 52 68
Table 1. Recommended number of gillnets according to size
and depth of the lake. Depth is divided into four categories
(I–IV). Category I includes lakes with a maximum depth of
3 m and one depth zone (0–3 m). category II includes lakes
with a maximum depth of 10 m and two depth zones (0–3
m, 3–10 m), category III includes lakes with a maximum
depth of 20 m and three depth zones (0–3 m, 3–10 m,
10–20 m) and category IV includes lakes with a maximum
depth of > 20 m and four depth zones (0–3 m, 3–10 m,
10–20 m, > 20 m).
147

Table 2. Lake area (km 2 ), depth (m) and number of gillnets used in the test fishing.
Lake Area (km 2 ) Depth (m) Number of gillnets
Finland Harrijärvi 0.96 11 21
Pitkä Surnujärvi 0.69 11.3 22
Norway Durvatn 0.37 16 16
Gardsjøen 0.67 25 21
Holmvatn 0.80 >20 21
Rabbvatn 0.40 23 16
Rundvatn 0.45 15 23
Russia Shuonijaur 8.5 10 40
Ilja-Nautsijarvi 3.5 30
Virtuovoshjaur 1.16 13 26
Riuttikjaure 0.9 21
Toartesjaur 0.6 21
Table 3. Determination of ecological status for trout in acidified lakes based on the catch per unit effort (CPUE, number of fish per
100 m 2 ) and quality of the spawning and feeding habitat (OR) (Sandlund et al. 2013).
CPUE, number of fish per 100 m 2
Gillnet type OR Very good Good Moderate Bad Very Bad
NORDIC ≥ 50 > 20 20–5 15–10 < 10 < 5
NORDIC 25–50 > 15 15–10 10–5 5–2 < 2
NORDIC ≤ 25 > 10 10–5 5–2 < 2 0
Age determination was based on operculum
(perch), otoliths (trout and char in Norway), cleithrum
(pike in Russia) or scales (others). For the Finnish
lakes the back-calculation of growth is based on the
formula of Monastyrsky (Bagenal & Tesch 1978). For
the Russian lakes the growth of perch and pike was
back-calculated with the Lea formula and the growth
of whitefish was back-calculated with the Lee formula
(Chugunova 1959, Bryuzgin 1969).
For the Finnish lakes the ecological status was
evaluated by using the Finnish EQR4 index (Tammi et
al. 2006, Olin et al. 2013). Fish community variables
used in evaluating the ecological status were biomass
(CPUE, g/net), number (CPUE, number/net) and appearance
of indicator species. Ecological quality ratio
(EQR) was calculated by dividing the observed value
of each variable with lake type specific reference value.
Average from the EQR values of each variable describes
the fish community based on evaluated ecological
status of the lake.
For the Norwegian lakes the ecological status of
trout populations was evaluated based on a classification
system that takes into account the quality of the
available spawning and feeding habitats (OR = oppvekstratio)
and the ratio between their surface areas
(m 2 ) and the lake’s surface area (ha). In this study the
quality of spawning and feeding habitat was evaluated
from satellite pictures. Ecological status is evaluation
also dependent on catch per unit effort (CPUE,
as number of fish per 100 m 2 ) (Table 3). The classification
system describes the fish community on a
five step scale: Very good, Good, Moderate, Bad and
Very Bad. Quality of trout and char was also evaluated
based on muscle colour, presence of parasites and
condition factor.
For Russian lakes the ecological status was evaluated
based on expert assesment method in which the
studied lakes were compared to reference ecosystems.
Also the frequency of malformations in fish and
heavy metal accumulation in fish tissues was studied.
148

Results and discussion
Community structure
The community structures of the studied lakes were
quite similar in Finland and Russia (Figure 1).
The dominant fish groups overall were salmonids
and percids. The common salmonids were whitefish
(sparsely-rakered (SR) form in Russia, form was not
determined in Finland) and grayling, some of the lakes
also had trout and char. The only percid present
was perch. Pike, cyprinids, minnow and burbot were
caught in some of the lakes but the numbers were
low. In the Norwegian lakes that were sampled in
2013 trout and char were the main species (Figure 1)
but also the presence of stickleback was suspected in
most lakes. However, after the first field work in 2013
it was decided to include one more lake in the survey,
in order to compare community structure of perch
across borders. An additional “perch lake” was therefore
chosen in Norway.
The fish communities in the lakes are different between
Russia, Finland and Norway due to the historical
immigration of fish after the previous ice age
(Økland 1966). After the ice age (8 000 years ago)
the big Ancylus lake was established. This lake covered
the Baltic Sea and parts of Finland and Sweden.
Fish species like perch, pike and whitefish emigrated
from the Ancylus Lake into rivers and lakes in the inland
part of Finnmark, Northern Finland, Sweden and
Northwest Russia. Trout and char immigrated to the
coastal lakes and rivers along the Norwegian coast.
CPUE in Finland
The total CPUEs as g/net and number/net of each
study lake were evaluated (Table 4, Figure 2 and Figure
3). Lake Harrijärvi had a higher g/net CPUE of
the Finnish lakes and Lake Pitkä Surnujärvi had more
individuals per net. Fish community of Lake Harrijärvi
consisted mainly of large-sized individuals of grayling.
Domination of salmonids is typical of the oligotrophic
lakes in the northern Finland. In Lake Pitkä Surnujärvi
the biomass was dominated by perch, but in numbers
more salmonids were caught. Fish community of Lake
Pitkä Surnujärvi mainly consisted of large perches
and small whitefish.
CPUE in Norway
Most of the Norwegian lakes had CPUEs similar to
Finnish lakes and the catch consisted of trout and Arctic
char. The highest CPUEs of all study lakes were in
Lake Rundvatn (3846 g/net and 38.3 individuals/net)
(Table 5, Figure 2 and Figure 3). The catch in Lake
Rundvatn was clearly dominated by perch. The quality
of the trout in Rundvatn was very good with a condition
rate of 1.1 and the color of the muscle tissue is
mainly red or pink. Trout larger than 25 cm are considered
as piscivorous and therefore have plenty of small
perch to feed upon. There are only small tributaries to
Lake Rundvatn and the spawning possibilities for trout
are considered as poor. The perch population is very
dense but the fish still grow to lengths of > 15 cm.
CPUE in Russia
The CPUEs of Russian lakes were similar to, or lower
than in the Finnish and Norwegian lakes (Table 6, Figure
2 and Figure 3). Lake Shuonijaur had low CPU-
Es. Percids dominated in the catch but in a previous
test fishing in 2005 trout and Arctic char were the main
species. This kind of change is typical of the water
reservoirs in the Murmansk Region. The ratio of mature
and immature Arctic char specimens in different
age groups testifies of a significant number missing
spawning. It is evident that in Lake Shuonijaur effective
reproduction of char is impeded by anthropogenic
load, lack of favorable food reserves and competition
from perch.
In lakes Ilja-Nautsijarvi, Virtuovoshjaur and Toartesjaur
percids dominated in the catch of 2013. However,
earlier test fishings in Virtuovoshjaur in 1992
and 2005 indicated that whitefish was the dominant
species. Present whitefish population in Virtuovoshjaur
consists mainly of younger age groups for which
early maturity is common.
In Virtuovoshjaur the average weight of perch was
100–150 g and length 18–24 cm In Ilja-Nautsijarvi
perch were of a smaller size (up to 100 g and 10–15
cm). In Lake Toartesjaur there were quite a lot of juvenile
perch but also mature fish of all size classes were
caught. In Lake Riuttikjaure the biomass was dominated
of salmonids but small-sized percids were more
abundant in numbers.
CPUE values for most of the study lakes can be
deemed low. A high proportion of predatory fish was
also noted in the structure of Russian fish populations.
The biomass of predators in some lakes reached 64
% (Virtuovoshjaur), 84 % (Ilja-Nautsijarvi) and even
92 % (Toartesjaur).
In Lake Kochejaur in Russia the fish community
has been studied in the period from 1989 to 2010.
The proportion of perch was small in 1989 and whitefish
has historically been the dominant species. New
studies revealed an elevated proportion of perch. In
149

0.3
4.3
10.3
8.7
49.9
24.4
7.8
29.9
whitefish
grayling
pike
perch
trout
81.1
Harrijärvi
17.9
Pitkä Surnujärvi
Pitkä Surnujärvi
5.1
60.4
char
burbot
31.4
22.3
46
Shuonijaur
12
68.8
77.7
54
88
Durvatn
Holmvatn
Rabbvatn
Rundvatn
0.3
4.3
9.5
26.4
19.8
1.9
5.6
5.1
29.9
60.4
62.2
28.3
60.7
12.6
0.3
23.5
56.7
82.5
Shuonijaur
Ilja-Nautsijarvi
Virtuovoshjaur
Riuttikjaure
Toartesjaur
Figure 1. The proportions (biomass %) of different fish species in the studied lakes. Minnows were caught in lakes Pitkä-Surnujärvi
and Ilja-Nautsijarvi, but their biomass proportions were so low that they rounded to zero.
Table 4. Summary of the results from the gillnet fishing in the Finnish lakes.
Lake
Harrijärvi
Pitkä Surnujärvi
Fish species
Total catch
(number)
Number
%
Total catch
(g)
Biomass
%
CPUE
g/net
CPUE
number/net
CPUE (per
100 m 2 )
Pike 9 12 2699 10.3 128.5 0.4 0.9
Whitefish 28 40.6 2285 8.7 108.8 1.3 2.9
Grayling 32 46,4 21352 81.1 1016.8 1.5 3.3
Total 69 100 26336 100 1254.1 3.2 9.8
Perch 50 41.3 6936 49.9 315.3 2.3 5.1
Pike 9 7.4 2494 17.9 113.4 0.4 09
Whitefish 55 45.5 3390 24.4 154.1 2.5 4.9
Grayling 6 5.0 1084 7.8 49.3 0.3 0.7
Minnow 1 0.8 3 0.0 0.1 0.1 0.2
Total 121 100 13907 100 632.2 5.6 11.8
150

CPUE g/net
0 500 1000 1500 2000 2500 3000 3500 4000 4500
Finland
Harrijärvi
Pitkä Surnujärvi
632
1254
Norway
Durvatn
Holmvatn
Rabbvatn
Gardsjøen
Rundvatn
481
742
1063
1210
3846
Russia
Shuonijaur
Ilja-Nautsijarvi
Virtuovoshjaur
Riuttikjaure
Toartesjaur
259
203
459
1355
1599
Figure 3. The total CPUE g/net in
the study lakes.
Finland
Norway
Harrijärvi
Pitkä Surnujärvi
Durvatn
Holmvatn
Rabbvatn
Gardsjøen
Rundvatn
3,3
5,5
5,2
3,6
7,7
9,7
CPUE number/net
0 5 10 15 20 25 30 35 40 45
38,3
Russia
Shuonijaur
Ilja-Nautsijarvi
Virtuovoshjaur
Riuttikjaure
Toartesjaur
1,9
5,6
7,1
9,0
12,1
Figure 2. The total CPUE number/
net in the study lakes.
Table 5. Summary of the results from the gillnet fishing in the Norwegian lakes.
Lake
Durvatn
Holmvatn
Rabbvatn
Gardsjøen
Rundvatn
Fish species
Total catch
(number)
Number
%
Total
catch (g)
Biomass
%
CPUE
g/net
CPUE
number/net
Trout 90 73.2 11666 68.6 729 5.6 12.5
Arctic char 33 26.8 5340 31.4 334 2.1 4.6
Total 123 100 17006 100.0 1063 7.7 17.1
Trout 126 61.8 19725 77.7 939 6.0 13.3
Arctic char 78 38.2 5678 22.3 270 3.7 8.2
Total 204 100 25403 100 1210 9.7 21.6
Trout 34 41.0 4151 54.0 259 2.1 4.7
Arctic char 49 59.0 3542 46.0 221 3.1 6.8
Total 83 100 7693 100 481 5.2 11.5
Trout 76 100 15590 100 742 3.6 8.0
Total 76 100 15590 100 742 3.6 8.0
Trout 30 3.8 9100 12.0 446 1.5 3.3
Perch 750 96.2 69000 88.0 3400 36.8 81.8
Total 780 100 78100 100 3846 38.3 95.1
CPUE (per
100 m 2 )
151

Table 6. Summary of the results from the gillnet fishing in the Russian lakes.
Lake
Fish species
Total catch
(number)
Number
%
Total catch
(g)
Biomass
%
CPUE
g/net
CPUE
number/net
CPUE (per
100 m 2 )
Trout 3 1.3 525 5.1 13.1 0.1 0.2
Perch 208 88.9 6256 60.4 156 5.2 11.6
Shuonijaur
Ilja-Nautsijarvi
Virtuovoshjaur
Riuttkijaure
Toartesjaur
Arctic char 20 8.5 3096 29.9 77.4 0.5 1.2
Pike 1 0.4 441 4.3 11 0.03 0.1
Burbot 2 0.9 32 0.3 0,8 0.1 0.2
Total 234 100 10350 100 259 5.93 13.3
Perch 231 85.5 25293 62.2 843 7.7 17.1
Whitefish 25 9.3 3859 9.5 129 0.8 1.8
Pike 12 4.4 11495 28.3 383 0.4 0.9
Minnow 2 0.7 3 0.0 0.1 0.1 0.2
Total 270 100 40650 100 1355 9 20
Perch 205 65.1 25228 60.7 970 7.9 17..5
Whitefish 103 32.7 10963 264 422 4.0 8.9
Pike 6 1.9 5241 126 202 0.2 0.4
Grayling 1 0.3 130 0.3 5.0 0.04 0.1
Total 315 100 41562 100 1599 12.14 26.9
Perch 31 795 1001 23.5 47.7 1.5 3.3
Whitefish 6 15.4 2415 56.7 115 0.3 0.7
Trout 2 5.1 843 19.8 40.1 0.1 0.2
Total 39 100 4259 100 203 1.9 4.2
Perch 145 97.3 8578 82.5 409 6.9 15.2
Pike 3 2.0 582 5.6 27.7 0.1 0.2
Burbot 1 0.7 1235 11.9 58.8 0.05 0.1
Total 149 100 10395 100 495 7.15 15.5
the last test fishing perch formed the majority of the
fish population. The whitefish population had only
single specimens of younger age groups. The majority
was 4–7 year old fish, which is associated with a
high degree of fish migration from other water reservoirs,
using Lake Kochejaur mainly as a feeding area.
Absence of juvenile specimens is caused by silting
up of the water reservoir and absence of spawning
grounds.
Growth rate
Finland
The growth of grayling in Lake Harrijärvi was quite fast
(on average, the total length of 30 cm was exceeded
during the fourth growing season) whereas in Lake
Pitkä Surnujärvi 30 cm length was not exceeded before
the age of six. The growth rate seemed to be quite
constant in Lake Harrijärvi and in Pitkä Surnujärvi
during the first four growing seasons, but seemed to
slow down after the individuals exceeded the length
of 25 cm.
The growth of whitefish in Lake Harrijärvi was quite
fast as the total length of 30 cm was exceeded on
average during the third growing season but in Lake
Pitkä Surnujärvi the growth was fairly slow. The
growth rate in Harrijärvi was quite constant during the
first three growing seasons but seemed to slow down
after fish exceeded the length of 30 cm, whereas in
Pitkä Surnujärvi the growth rate seemed to stay the
same during all growing seasons.
The growth of perch in Lake Pitkä Surnujärvi was
moderate when compared to other northern lakes
(Sairanen et al. 2007) as the total length of 20 cm was
exceeded during the sixth or seventh growing season,
on average.
Norway
The growth rates for trout and Arctic char were relatively
good the first 4–5 years in Lake Durvatn and
Lake Gardsjøen. In lakes Rabbvatn and Holmvatn the
152

growth rate for trout and Arctic char was lower compared
to lakes Durvatn and Gardsjøen. In all the lakes
the growth slowed considerably when the fish matured,
which is normal. The age for maturity varied between
the different lakes but both trout and Arctic char
population in all the lakes started to mature at the age
of four or five years old.
Russia
In the small Russian lakes short lifetime and early maturity
of fish was noted despite their apparent remoteness
from industrial pollution (Chapter 4, Introduction,
Figure 1). Fish older than seven years are encountered
in single specimens. The growth rates have decreased
compared to earlier research in most lakes. In
Lake Shuonijaur perch has higher growth rate than
char. The growth rate of perch is high until the length
of 20 cm but is then reduced, which can be explained
by the transition to the combined and predatory type
of feeding. The growth rate of char seemed to be quite
constant at all ages. In Lake Ilja-Nautsijarvi the growth
rates of all the fish species are low, for example the
whitefish reach the size of 30 cm only at the age of
7–8 years. In Lake Virtuovoshjaur and Lake Riuttkijaure
the growth rates of whitefish and perch are even
somewhat lower than in Lake Ilja-Nautsijarvi.
In all of the Russian study lakes the whitefish (SR)
grows fastest during the first year of life and the growth
decreases when they reach the age of 2+. Whitefish
of lakes Virtuovoshjaur and Ilja-Nautsijarvi have periods
of even slower growth at the age of 7+ which
accords with literature (Reshetnikov, 1966, 1980). The
growth rate of the perch was noted to slow at the age
of nine. In Lake Toartesjaur the growth rate of perch is
average until the age of four and then it drops until the
age of seven when it seems to start growing again.
Ecological status
According to test fishing results 2013 the fish community
based ecological status of Lake Harrijärvi was
good and Lake Pitkä Surnujärvi high. In the case of
Lake Harrijärvi the number catch and indicator species
indicated high ecological status whereas the biomass
catch indicated only moderate ecological status.
The high biomass catch compared to lake type
(Finnish lake type Vh = Small and medium sized clear
water lakes) specific reference value resulted from too
high biomass catch of grayling that lead only to good
overall ecological status. However, because grayling
is one of the indicator species, Lake Harrijärvi should
also be considered in the highest class (Table 7).
The ecological status of trout populations in the
Norwegian lakes was classified as good in Durvatn,
Holmvatn and Gardsjøen and moderate in Rabbvatn
and Rundvatn. In Norway a method for ecological
classification of fish communities other than trout has
not been developed yet.
The method of lake ecological status classification
based on the fish communities’ condition is not developed
in Russia. However, long-time experience of
the researchers (INEP) made possible to assess the
status of the studied Russian lakes based on comparisons
with “reference water ecosystems”. According to
this expert assessment method, the ecological status
of Lake Virtuovoshjaur was high and Lake Ilja-Nautsijarvi
good. The other lakes´ status was classified as
moderate.
Table 7. Ecological status of the fish populations in lakes in the border region
according to national standards.
Country Lake Ecological status
Finland
Russia
Norway
Harrijärvi
Pitkä Surnujärvi
Shuonijaur
Ilja-Nautsijarvi
Virtuovoshjaur
Riuttikjaure
Toartesjaur
Durvatn
Gardsjøen
Holmvatn
Rabbvatn
Rundvatn
High (very good)
High (very good)
Moderate
Good
High (very good)
Moderate
Moderate
Good
Good
Good
Moderate
Moderate
153

Malformations in fish and assessment of anthropogenic
load
Malformations due to environmental pollution were
noted in different species of fish in all the Russian lakes,
as fish muscle, liver, kidneys and gills were analyzed
for copper, nickel, mercury and zinc. The main
affected organs were liver and kidneys but also changes
in gonads and gills were detected. The common
changes included pale color of liver (fatty degenerations),
formation of excess connective tissue in kidneys
and gonads, segmented structure and asynchronous
maturity of reproductive products and distortion
of gill rakers in whitefish. In general, the frequency of
occurrence and intensiveness of fish pathologies stay
on the same level with insignificant changes throughout
the whole term of observations (Figure 4). The
malformations are caused by toxic impact of heavy
metals.
Copper concentrations have either grown in the last
decades (more common) or stayed at the same level.
Concentrations are highest in whitefish, especially in
the bottom-feeding sparsely-rakered type, and in all
studied species it accumulates most in the liver and
kidneys. Nickel, in difference to copper, demonstrates
a reduction of accumulation in fish over the last years.
The differences between species and organs are not
as clear as with copper but it seems that whitefish is
the most affected species and kidneys and gills the
most accumulating organs.
Mercury tends to accumulate more in the liver and
kidneys than in the muscle and highest contents of
mercury were naturally recorded in predatory fish. Exceeded
maximum allowable concentrations of mercury
were noted in muscle tissue of perch and pike
throughout the whole observation period in the most
distant lakes Kochejaur and Virtuovoshjaur (Figure 5).
Zinc enhances the toxicity of many other metals.
Its concentrations did not exhibit time trends or interspecies
trends in any of the water reservoirs and there
is also a significant variability of zinc accumulation in
fish organs within each species.
High variability of the heavy metal content within
one species was recorded practically in all the water
reservoirs. For some specimens the levels of copper,
nickel and zinc accumulation were comparable to
and exceeded the maximum values of metals’ concentrations
in the fish of Lake Kuetsjarvi, which is regarded
as the most polluted water body of the Pasvik
watercourse. This can testify of the retaining airborne
anthropogenic load in the border area and impact of
the secondary pollution from bottom sediments.
Malformations were not studied in Finnish and Norwegian
lakes’ fish. Norwegian lakes have suffered
from acidification quite recently but the situation has
improved. The quality of trout and char was evaluated
based on the color of muscle and presence of parasites.
The quality was good in Holmvatn and Rundvatn,
moderate in Gardsjøen and relatively poor in Rabbvatn.
Kochejaur
Virtuovoshjaur
frequency occurance, %
100
80
60
40
20
frequency occurance, %
100
80
60
40
20
changes of gonads
changes of kidneys
changes of liver
changes of gills
0
0
1991 2002 2005 2007
1992 2005 2007 2013
Figure 4. Frequency of malformations of whitefish in lakes Kochejaur and Virtuovoshjaur.
154

4,5
4,0
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0
muscle
MPC for Hg
Wh Wh Pe Pi Wh Pe Pi Wh Pe Pi
4,5
4,0
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0
muscle
MPC for Hg
Wh Pe Pi Wh Pe Pi Wh Pe Pi
2002 2005 2007 2009
2005 2007 2013
Kochejaur Lake
Virtuovoshjaur Lake
Figure 5. Mercury in fish muscle in lakes Kochejaur and Virtuovoshjaur over various periods of research (µg/g dry weight
/ppm). MPC =
maximum permissible concentration, Wh = whitefish, Pe = perch, Pi = pike.
Summary and findings
Fish communities of small border area lakes in Finland,
Russia and Norway were studied in 2013 and
2014 and the ecological status was evaluated based
on fish community variables. The final aim was to
evaluate the usefulness of fish community variables
(community structure, growth rate, maturation age,
longevity) in assessment of environmental impacts.
Based on earlier studies carried out in small lakes
in the border area, several new variables were included
in order to assess impacts of climate change, hazardous
substance deposition and possible effects of
acidification. One of the main reasons to conduct fish
community studies was to carry out a baseline study
using the methods required in the EU Water Framework
Directive.
Fish community studies give information on how
the community structure changes over time. Fish community
variables measured in this study give a clear
indication of how fish communities change across
borders and over a long time period.
Lakes of Arctic and subarctic latitudes, as a rule,
do not have a variety of fish species as species
richness decreases as latitude increases (Hillebrand
2004). Arctic char, trout, whitefish and perch are the
most common species in the border area of Finland,
Russia and Norway.
Community structure
Finnish and Norwegian lakes are highly oligotrophic
clear water lakes and their water quality is generally
high. The fish communities represent the typical
fish community of small sized lakes in northern areas
where the number of species is low and salmonid fishes
are dominant. No signs of environmental degradation
were detected and occurrence of several yearclasses
of most species indicated no signs of failure
in reproduction. No harmful effects of acidification on
the fish communities of surveyed lakes were observed.
Minnow, which is highly sensitive to acidification,
existed in Lake Pitkä Surnujärvi For further monitoring,
electrofishing of stony shores could be included
to obtain a more reliable figure of minnow population.
The studied Russian lakes are all oligotrophic and
the composition of fish community is typical for such
arctic lakes even though the predominant species are
changing from coregonids and other salmonids to
perch. A fast restructuring of fish community of water
reservoirs of the Murmansk Region in the last decade
was noted: perch and cyprinids often replace salmonids.
The number of perch in the lakes over the
last decade has a constant tendency to grow, and in
a number of lakes its proportion can reach over 90 %.
This change in the fish community is evident in Norway,
in the Pasvik River lakes, where an increase of
perch most likely is related to observed temperature
increases (Chapter 3, Long-term effects of metal contaminations,
water regulations, species invasions and
climate change on the fish community of the Pasvik
River). In addition to climate change, also high level
of loading from the indusrty, the catchments and bottom
sediments may influence this change in species
communities (Kashulin et al. 2012, Terentjev & Kashulin
2012).
Growth rate and maturation age
In the Finnish lakes the growth rates were quit fast
and constant in both lakes. The growth rates in most
lakes in Russia have decreased compared to earlier
studies. In Norway the growth rate of trout was normal.
The growth rate and maturation age varied for
155

fish in the Norwegian lakes. In all the lakes the growth
rate slowed considerably when the fish became mature
at the age of four to six years. There seems to
be a good correlation between growth rate, population
density and lake morphology.
The studied Russian lakes are all oligotrophic and
the composition of fish community is typical for such
arctic lakes even though the predominant species are
changing from coregonids and other salmonids to
perch. The levels of priority toxicants copper, nickel,
zinc and mercury are all elevated, which shows especially
in whitefish through changes in life cycle and
multiple malformations.
For future studies of the fish communities of small
border area lakes it is recommended that monitoring
should be continued with the NORDIC nets and same
methods. Monitoring should be conducted at a threeyear
interval. Electrofishing of stony shores could be
included to obtain a more reliable figure of minnow population
and other species or size classes living in the
shoreline in all of the study lakes. Monitoring of heavy
metal levels and especially mercury in fish is recommended.
The evaluation of the ecological status of the
lakes based on fish communities should be done in an
as uniform way as possible.
References
Bagenal, T.B. & Tesch, F.W. 1978: Age and growth. In: Bagenal T.(ed.): Methods for assessment of fish production in fresh
waters. Blackwell, Oxford. p. 101–136.
Bryuzgin V.L. 1969: Methods of study of fish growth by scales, bones and otoliths. Naukova Dumka. Kiev. 188 p.
Chugunova N.I. 1959: Fish age and growth study guide. Publisher of USSR Academy of Science. 164 p. (in Russian)
Hillebrand, H. 2004: On the generality of the latitudinal diversity gradient. The American Naturalist 163 (2): 192–211.
Kashulin N.A., Denisov D.B., Valkova S.А., Vandysh O.I., Terentjev P.M. 2012: The modern tendencies of modification of
fresh water ecosystems of the Euro-Arctic region. Proceedings of Kola science center. Applied ecology of the North. 1:
7-54. Kola Science Center RAS. (in Russian).
Kurkilahti, M. 1999: Nordic multimesh gillnet – robust gear for sampling fish populations. Academic dissertation, Department
of Biology, University of Turku.
Olin, M., Rask, M., Ruuhijärvi, J. & Tammi, J. 2013: Development and evaluation of the Finnish fish-based lake classification
method. Hydrobiologia 713: 149–166.
Reshetnikov Yu. S. 1966: Peculiarities of growth and maturing of whitefish in Northern water basins. In: Nikolsky, G.V.,
Lapin, J.E. (ed.) Regularities of fish population dynamics in the White Sea and its basin. Nauka. Moscow. p. 93–155. (in
Russian)
Reshetnikov, Yu 1980: Ecology and systematics of coregonid fish. Nauka. Moscow. 300 p. (in Russian)
Sairanen, S., Rask, M., Stridsman, S. & Holmgren, K. 2007: TRIWA II work report, Fish communities of 15 lakes in River
Torne basin: aspects of lake typology and ecological status. Work report. 45 p.
Sandlund, O.T. 2013: Vannforskriften og fisk – forslag til klassifiseringssystem. Miljødirektoratet Rapport M22-2013. (in
Norwegian)
Tammi, J., Rask, M. & Olin, M. 2006: Kalayhteisöt järvien ekologisen tilan arvioinnissa ja seurannassa. Alustavan luokittelujärjestelmän
perusteet. Kala- ja riistaraportteja 383. 51 p. (in Finnish)
Terentjev P.M., Kashulin N.A. 2012: The transformation of fish communities in the waterbodies of the Murmansk region.
Proceedings of Kola science center. Applied ecology of the North 2: 61–100. Kola Science Center RAS. (in Russian).
Økland, J. 1966. Elg og stor damsnegl levde i Løten for 8 000 år siden. Fauna 19:158-25. (in Norwegian)
156

Photos: Helén Johanne Andersen
157

158

Chapter 5: Influence of pollution and
climate variation in small rivers indicated
by freshwater pearl mussels
PAUL ERIC ASPHOLM, MICHAEL CARROLL, GUTTORM N. CHRISTENSEN, HELÉN JOHANNE ANDERSEN, WILL
AMBROSE
Freshwater pearl mussels. Photo: Helén Johanne Andersen.
159

1 Freshwater pearl mussel –
The environmental storyteller
The objectives of this investigation are to establish a
method of revealing the influence of climate parameters
and heavy metal pollutants in rivers using shells
of freshwater pearl mussels for long time series. The
effects of climate change on the hydrological regime
of the rivers and further on the ecological status of
the EU Water Framework Directive bioindicator and
flagship species Margaritifera margaritifera have been
evaluated from three rivers in two different climatic zones
in Sør-Varanger County in Norway.
The freshwater pearl mussel (Margaritifera margaritifera)
is a long-lived aquatic organism, which may
reach an age up to 150–200 years (Bauer 1992, Ziuganov
et al. 2000, Schöne et al. 2004, Helama & Valovirta
2008). It occurs in clear, flowing streams and rivers
throughout northern Europe (Skinner et al. 2003).
Overall abundances and populations have declined
dramatically over the past century due to harvesting
(for the shell and pearls) and habitat degradation
(Bauer 1986, 1988), and the mussel is currently listed
on the IUCN red list as severely threatened.
The hard parts of aquatic organisms (e.g. coral
skeletons, shells of clams, fish otoliths) are deposited
sequentially as the organism grows (Morrongiello et
al. 2012). Consequently, the sequential deposition of
hard parts by an animal records a biological and environmental
chronology during its life, and the study
of this record, sclerochronology, has been fundamental
in reconstructing past environments (Hudson et al.
1976, Wanamaker et al. 2012).
The freshwater pearl mussel shell grows through
sequential accretion of calcium carbonate material deposited
at the shell margin (Mutvei et al. 1994, Dunca
1999). Seasonal changes in growth result in annual
growth increments in the shell structure (similar to
tree-rings). The size of the annual rings is affected by
nutrients, pH, availability of food, availability of calcium
and climatic and environmental factors such as
water temperature and turbidity. This allows us to use
the growth of the mussel as a proxy for different environmental
conditions (Mutvei et al. 1996, Dunca et
al. 2005, Black et al. 2010). The shells are excellent
archive indicators of environmental changes, as they
have solid and impermeable shells that retain incorporated
elements from the ambient water without spatial
relocation. This allows reconstruction the history of
impacts like climate and pollution experienced over lifespans
that can be well in excess of 100 years (Westermark
et al. 1996), predating instrumental records
in the area. The analysis of shell increments makes
it possible to establish long-term growth chronologies
and assess environmental conditions in different years
(Jones et al. 1989, Schöne et al. 2003, Ambrose et
al. 2006).
The freshwater pearl mussel is a key species in
northern Norwegian rivers, especially in those containing
Atlantic salmon and brown trout. The mussels
are natural biological filters and one mussel can filter
up to 50 liters of water per day (Hendelberg 1961).
This reduces organic matter in the system, and hence
helps to maintain oxygen levels in the interstitial
water, which would be reduced through bacterial degradation
of excess organic material on the bottom.
Therefore, M. margaritifera is an effective indicator of
the temporal variation in local pollution levels (Mutvei
et al. 1996, Mutvei & Westermark 2001).
High levels of bioaccumulated metals have been
found in the shells of M. margaritifera from several
European countries, and toxic heavy metals (copper
(Cu), lead (Pb), arsenic (As), nickel (Ni), and chromium
(Cr)) are regarded to have contributed to the
decline of freshwater pearl mussel populations. The
same applies to the essential metals iron (Fe), manganese
(Mn), and zinc (Zn). Cadmium (Cd) and Cu
are predominantly found in the viscera; Mn, Ni, Pb,
Mg, and Zn are mainly found in the combined other
tissues, while Fe, As and Cr are evenly distributed
between both fractions. Cu is a toxic metal to all
aquatic organisms and is bioaccumulated to a rather
high degree. Cu, Fe, Pb, Mg and As all interfere with
calcium metabolism by different mechanisms. These
heavy metals are also stored in the shell as it is secreted.
Hence, simultaneously analyzing shell metal
concentrations and growth patterns can lead to better
constraints on the temporal history of metal inputs to
the environment and ecosystem (Carroll et al. 2009).
160

Methods and key findings
30 living mussels were collected in 2012–2013 from
each of the three rivers; Karpelva, Skjellbekken and
Spruvbekken. The rivers are located in different climatic
zones, enabling a comparison of growth of the
mussel under variable climatic conditions. The M.
margaritifera populations occur at various distances
and directions from the Nikel combine at the Kola Peninsula
in Russia. The freshwater pearl mussel is currently
listed by the IUCN red list as severely threatened
and it is therefore a challenge to collect mussels
for investigation. However, this study has also taken
into consideration the use dead shells to reveal new
knowledge about pollution and climate variations.
The bivalve shells were sectioned dorso-ventrally
along the axis of minimum growth starting at the hinge
point of the shell. Then these cross sections were
imaged and advanced imaging microscopy software
was used to enumerate growth lines for growth rates
in order to identify placement of sample sites for mineral,
including heavy metal, analysis (Figure 1). Growth
and age were calculated from the growth rings, and
contaminant levels can be related to the increments,
providing an absolute time marker. Shells exhibited
annual growth distinguished from distinct lines signifying
the winter period of little or no growth (Figure 1).
This study determined that individuals from Karpelva
(N=30) ranged in age from 60 to 220 years. Preliminary
growth chronology was developed, which exhibits
a variable shell growth pattern among years (Figure
2). The role of different environmental factors regulating
these growth patterns (e.g. river temperature, air
temperature, precipitation, wind etc.) will be studied
further.
Tissue was dissected from shells and frozen for
analysis of contaminants and stable isotopes.
Figure 1. The growth increments of the M. margaritifera,
which are produced with an annual cycle. These
increments form the temporal basis of the chronology.
Figure 2. Growth chronology of the freshwater pearl mussels from the collection point in 2012 back to 1860, spanning
some 150 years. Oscillatory periods of higher and lower growth are evident.
161

Shell and tissue isotopic ratios (δ 18 O, δ 13 C and
δ 15 N) can be proxies for water temperatures and food
sources, respectively. Heavy metal concentrations in
tissues and shells varied among individuals (Figure
3), but overall exhibited higher levels at Karpelva
compared to other river systems farther afield from
the Nikel plant (Figure 4).
Thin (1 mm) sections of the shell were prepared for
geochemical analysis, followed by measurement of
elemental ratios within the prismatic layer (Figure 5).
The heavy metal concentrations in the shell material
varied among time-periods with elevated concentrations
of some elements after the Nikel plant came into
operation (in the 1930s), compared with earlier (Figure
6). Iron and manganese particularly have trends
of increased concentrations in growtn increments formed
during the plant operation compared to before.
Nickel
Copper
Iron
Concentration (ug/g)
50
40
30
20
10
Shell
Tissue
40
30
20
10
2000
1500
1000
500
0
0 5 10 15 20 25 30
0
0 5 10 15 20 25 30
0
0 5 10 15 20 25 30
Sample Number
Sample Number
Sample Number
Figure 3. Heavy metal concentrations (Ni, Cu, Fe) in bulk shell and tissues from Karpelva.
16
Nickel
14
12
Shell
Tissue
Concentration (ug/g)
10
8
6
4
2
0
Karpelva SkjellbekkenSpurvbekken Føllelva Juojoki
Figure 4. Comparison of nickel concentration (mean +SErr) in bulk
shell and tissues from Karpelva and other river systems.
162

Conclusions
The results from this study show that the freshwater
pearl mussel (Margaritifera margaritifera) can be
used to investigate climate parameters and heavy
metal pollutants for long time series in rivers. However,
more investigation is needed to harmonize and
calibrate the annual growth chronology and to relate
growth differences among years to local climate and
large-scale climate indices.
The project has also evaluated the possibilities to
use mussels that have died of natural causes for monitoring
of contaminants and climatic variations. It is
possible, but it is beneficial that the mussels are relatively
fresh and that the approximate time of death
is known.
Figure 5. Image of the shell margin area of a freshwater pearl mussel after laboratory processing (sectioning and polishing).
(Image: W. Locke)
Figure 6. Concentration of various metals in the
M. margaritifera shells from Karpelva (site 1 =
upper figure, site 2 = lower figure), separated in
periods before and after the beginning of operation
of the Nikel plant in Russia in 1946.
163

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164

Freshwater pearl mussel investigations. Photos: Helén Johanne Andersen.
165

D O C U M E N T A T I O N P A G E
Publication series and numbers
Reports 41/2015
Area(s) of responsibility
Environment and Natural Resources
Author(s)
Jukka Ylikörkkö
Guttorm, N. Christensen
Nikolay Kashulin
Dmitrii Denisov
Helén Johanne Andersen
Elli Jelkänen
Date
May 2015
Publisher
Centre for Economic Development, Transport and the Environment for Lapland
Financier/commissioner
Kolarctic ENPI CBC Programme
Centre for Economic Development, Transport and the Environment for Lapland
Title of publication
Environmental Challenges in the Joint Border Area of Norway, Finland and Russia
Abstract
This report examines the human impact on the subarctic environment of the joint border area of Norway, Finland and Russia. The aim is
to present the current state and recent changes that have taken place in the region.
The main threat to the environment is the Pechenganikel mining and metallurgical industrial combine in the towns of Nikel and Zapolyarny
in the Kola Peninsula. Emissions from this complex include high levels of heavy metals, persistent organic pollutants and sulfur
dioxide. Pollution, along with climate change, water level regulation and other anthropogenic effects, has affected the aquatic ecosystems
in the joint border area.
The main heavy metals in the area are copper and nickel, the highest concentrations of which are measured near the combine. Direct discharge
of sewage into the river continues and airborne heavy metal particles are also deposited to areas farther away. Climate changeinduced
increase in temperature and precipitation in the Kola Peninsula is evident. Water level regulation with seven hydropower plants
in the Pasvik River have changed it into a series of lakes and lake-like reservoirs.
This report discusses modelling, which was enabled to estimate the effect of climate change on Lake Inarijärvi and the Pasvik River hydrology,
water level fluctuation and ecology and to follow the sulfur dioxide emissions emitted from the Pechenganikel. Effects of pollution
on the nature and concentrations of the main pollutants were studied and climate change in the border area and its effects on the ecology
were estimated. Also the effects of water level regulation on the ecological status of the aquatic ecosystems were addressed.
Keywords
Environment, monitoring, air quality, climate change, water quality, aquatic, ecosystems, the Pasvik River, heavy metals, POPs
ISBN (print)
978-952-314-258-9
www
www.doria.fi/ely-keskus
ISBN (PDF)
978-952-314-259-6
ISSN-L
2242-2846
ISSN (print)
2242-2846
URN
URN:ISBN:978-952-314-259-6
Distributor
Centre for Economic Development, Transport and the Environment for Lapland
P..O. Box 8060, FI-96101 Rovaniemi, Finland
Tel. +358 40 526 2821. Fax +358 16 310 340
Publication is also available in internet: www.doria.fi
Place of publication and date
Rovaniemi 2015
Printing place
Juvenes Print
ISSN (online)
2242-2854
Language
English
Number of pages
165
166

K U V A I L U L E H T I
Julkaisusarjan nimi ja numero
Raportteja 41/2015
Vastuualue
Ympäristö ja luonnonvarat
Tekijät
Jukka Ylikörkkö
Guttorm, N. Christensen
Nikolay Kashulin
Dmitrii Denisov
Helén Johanne Andersen
Elli Jelkänen
Julkaisuaika
Toukokuu 2015
Kustantaja /Julkaisija
Lapin elinkeino-, liikenne- ja ympäristökeskus
Hankkeen rahoittaja / toimeksiantaja
Kolarctic ENPI CBC -ohjelma
Lapin elinkeino-, liikenne- ja ympäristökeskus
Julkaisun nimi
Environmental Challenges in the Joint Border Area of Norway, Finland and Russia
(Ympäristöhaasteet Norjan, Suomen ja Venäjän yhteisellä raja-alueella)
Tiivistelmä
Tässä raportissa tarkastellaan ihmisten aiheuttamia haittoja Norjan, Suomen ja Venäjän yhteisellä, subarktisella raja-alueella. Raportissa
esitetään ympäristön nykytila ja alueella tapahtuneet viime-aikaiset muutokset.
Suurin uhka ympäristölle on Nikkelissä ja Zapolyarnyissa Kuolan niemimaalla sijaitseva Petsenganikelin kaivos- ja metalliteollisuuskombinaatti.
Laitoksesta leviää ympäristöön suuria määriä raskasmetalleja, pysyviä orgaanisia yhdisteitä ja rikkidioksidia. Saasteet, yhdessä
ilmastonmuutoksen, säännöstelyn ja muiden ihmisvaikutusten kanssa, ovat vaikuttaneet yhteisen raja-alueen vesiekosysteemeihin.
Alueen tärkeimmät raskasmetallit ovat kupari ja nikkeli, joiden ptioisuudet ovat korkeimmillaan kombinaatin lähellä. Suorat jätevesipäästöt
jokeen jatkuvat ja raskasmetallihiukkaset kulkeutuvat ilman mukana myös kaukaisemmille alueille. Kuolan niemimaalla on havaittavissa
ilmastonmuutoksen aiheuttama lämpötilan ja sademäärän nousu. Paatsjoen säännöstely seitsemällä vesivoimalalla on muuttanut joen
järvien ja järvimäisten patoaltaiden jatkumoksi.
Tässä raportissa käsitellään mallitusta, jolla arvioitiin ilmastonmuutoksen vaikutuksia Inarijärven ja Paatsjoen hydrologiaan ja veden
pinnankorkeuden vaihteluihin ja seurattiin Petsenganikelin rikkidioksidipäästöjen leviämistä. Saasteiden vaikutuksia luontoon ja päähaittaaineiden
pitoisuuksia tutkittiin ja ilmastonmuutosta raja-alueella ja sen vaikutuksia ekologiaan arvioitiin. Lisäksi havainnoitiin vedenkorkeuden
säännöstelyn vaikutuksia vesiekosysteemien ekologiseen tilaan.
Asiasanat (YSA:n mukaan)
Ympäristö, seuranta, ilmanlaatu, ilmastonmuutokset, vedenlaatu, vesiekosysteemi, raskasmetallit, pysyvät orgaaniset yhdisteet
ISBN (Painettu)
978-952-314-258-9
ISBN (PDF)
978-952-314-259-6
ISSN-L
2242-2846
ISSN (painettu)
2242-2846
ISSN (verkkojulkaisu)
2242-2854
www
www.doria.fi/ely-keskus
URN
URN:ISBN:978-952-314-259-6
Kieli
englanti
Sivumäärä
165
Julkaisun tilaukset
Lapin elinkeino-, liikenne- ja ympäristökeskus
PL 8060, 96101 Rovaniemi
Puh. +358 40 526 2821. faksi +358 16 310 340
Julkaisu saatavana myös verkossa: www.doria.fi
Kustannuspaikka ja -aika
Rovaniemi 2015
Painotalo
Juvenes Print
167

С Т Р А Н И Ц А Д О К У М Е Н Т А Ц И И
Название и номер серии публикации
Reports 41/2015
Ansvarsområde
Окружающая среда и природные ресурсы
Авторы
Юкка Юликоркко
Гутторм Н. Кристинсен
Николай Кашулин
Дмитрий Денисов
Хелен Йоханне Андерсен
Элли Йелканен
Дата публикации
Май 2015
Издатель
Центр экономического развития, транспорта и окружающей среды Лапландии
Финансирование/Заказчик
Программа Коларктик ИЕСП ПС
Центр экономического развития, транспорта и окружающей среды Лапландии
Название публикации
Environmental Challenges in the Joint Border Area of Norway, Finland and Russia
(Экологические проблемы общей приграничной территории Норвегии, Финляндии и России)
Резюме
Данный отчёт является обзором настоящего состояния окружающей среды и произошедших за последние годы изменений в
приграничном районе Норвегии, Финляндии и России. Основное внимание уделяется антропогенному воздействию на водную
среду.
Горно-металлургический комбинат ”Печенганикель”, расположенный на северо-западе Мурманской области, является одним
из самых крупных объектов, влияющих на состояние природной среды в приграничном районе. Выбросы комбината содержат
высокие концентрации тяжелых металлов, стойких органических загрязняющих веществ и диоксида серы. Загрязнение в
сочетании с климатическими изменениями, регулирование уровня воды и другие антропогенные факторы оказывают негативное
воздействие на водные экосистемы общей приграничной территории.
Из тяжелых металлов наиболее значимыми загрязнителями в этом районе являются медь и никель, самые высокие
концентрации которых наблюдаются вблизи комбината. Продолжаются сбросы токсических веществ в притоки реки Паз. Свой
вклад в загрязнение региона привносит и трансграничный перенос поллютантов. В связи с климатическими изменениями
отмечается рост температуры и количества осадков на Кольском полуострове. Семь ГЭС на реке Паз превратили ее в цепь озер
и водохранилищ озерного типа.
В публикации помещена информация о моделировании, позволяющем проследить за перемещениями выбросов диоксида серы
и оценить воздействие климатических изменений на гидрологический режим, колебание уровня воды озера Инари и реки Паз,
на их экологическое состояние. Продолжается изучение влияния регулирования на состояние водных экосистем в условиях
изменения климата.
Ключевые слова
Окружающая среда, мониторинг, качество воздуха, климатические изменения, качество воды, водный, экосистемы, река Паз,
тяжёлые металлы, СОЗ
ISBN (printed)
978-952-314-258-9
ISBN (PDF)
978-952-314-259-6
ISSN-L
2242-2846
ISSN (print)
2242-2846
ISSN (online)
2242-2854
www
www.doria.fi/ely-keskus
URN
URN:ISBN:978-952-314-259-6
Язык
Английский
Количество
165
Распространитель
Центр экономического развития, транспорта и окружающей среды Лапландии
а/я 8060, FI-96101 Рованиеми, Финляндия
Телефон: +358 40 562 2821, Fax +358 16 310 340
Публикация также доступна в Интернете по адресу: www.doria.fi
Место и год издания
Рованиеми, 2015
Издательство
Juvenes Print
168

P R E S E N T A T I O N S B L A D
Publikationens serie och nummer
Report 41/2015
Ansvarsområde
Miljø og naturresurser
Forfattere
Jukka Ylikörkkö
Guttorm, N. Christensen
Nikolay Kashulin
Dmitrii Denisov
Helén Johanne Andersen
Elli Jelkänen
Utgivelsedato
Mai 2015
Utgiver
Lappland nærinsutvikling, samferdsel og miljøsentral
Prosjektet er finansier av / oppdragsgiver
Kolarctic ENPI CBC program
Lappland nærinsutvikling, samferdsel og miljøsentral
Tittel / Publikasjonens tittel
Environmental Challenges in the Joint Border Area of Norway, Finland and Russia
(Miljøutfordringer i grenseområdet mellom Norge, Finland og Russland)
Sammendrag
Denne rapporten tar for seg miljøtilstanden i grenseområdet mellom Norge, Finland og Russland, på bakgrunn av industriell
aktivitet i området. Målet er å slå fast den nåværende miljøtilstanden og eventuelle nylige endringer.
Den største trusselen mot miljøet er gruve- og fabrikkanleggene i Nikel og Zapoljarnij. De forurensede utslippene fra smelteverkene
er tungmetaller, persistente organiske forbindelser (POPs), og svoveldioksid (SO 2
). Forurensning, klimaendringer, vannregulering,
og andre menneskeskapte påvirkninger har konsekvenser for det akvatiske økosystemet i grenseområdet.
Kobber og nikel er de mest framtredende tungmetallene som slippes ut fra smelteverkene, hvor de høyeste nivåene er målt
nærmest smelteverket. Smelteverket slipper ut avløpsvann til nærliggende elver og tungmetaller til luft som kan langtransporters
i atmosfæren. Det er tydelige tegn til klimaendringer, høyere temperaturer og mer nedbør på Kolahalvøya. Vannstanden i Pasvikelva
blir regulert med syv vannkraftverk, dette har resultert i at elva har fått et innsjøliknende preg.
Denne rapporten tar også for seg modellering for å kunne anslå effektene av klimaendringer i Enaresjøen og Pasvikelva.
Modelleringen vil også kunne anslå i hvilken grad endringer i vannstanden, økologi og SO 2
sluppet ut fra Penchenganikel har
på miljøet i regionen. På bakgrunn av en rekke miljøundersøkelser er konsekvensene av forurensede utslipp og klimaendringer
estimert. Hvilke konsekvenser vannregulering har på miljøtilstanden og det akvatiske miljøet er også blitt undersøkt.
Emneord
Miljø, overvåkning, luftkvalitet, klimaendringer, vannkvalitet, akvatisk, økosystem, Pasvikelva, tungmetaller, POPs
ISBN (trykk)
978-952-314-258-9
ISBN (PDF)
978-952-314-259-6
ISSN-L
2242-2846
ISSN (trykk)
2242-2846
ISSN (webbpublikasjon)
2242-2854
www
www.doria.fi/ely-keskus
URN
URN:ISBN:978-952-314-259-6
Språk
engelsk
Antal sider
165
Salg / Distributør av utgivelsen
Lappland nærinsutvikling, samferdsel og miljøsentral
PL 8060, FI-96101 Rovaniemi
Tel.+358 40 526 2821. Fax +358 16 310 340
Reporten finnes på Internet: www.doria.fi
Utgiversted og -år
Rovaniemi 2015
Trykksted
Juvenes Print
169

REPORTS 41 | 2015
ENVIRONMENTAL CHALLENGES IN THE JOINT BORDER AREA OF NORWAY, FINLAND
AND RUSSIA
Centre for Economic Development, Transport and the Environment for Lapland
ISBN 978-952-314-258-9 (print)
ISBN 978-952-314-259-6 (PDF)
ISSN-L 2242-2846
ISSN 2242-2846 (print)
ISSN 2242-2854 (online)
URN:ISBN:978-952-314-259-6
www.doria.fi/ely-keskus | www.ely-keskus.fi